{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#                   利用线性回归技术实现Ames房价预测\n",
    "     Ames房价预测是Kaggle平台上的一个竞赛任务，需要根据房屋的特征来预测亚美尼亚州洛瓦市的房价。\n",
    "    其中房屋的特征X共有79维，响应值y为每个房屋的销售价格。评价标准为预测值的对数和观测值的对数的RMSE。     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、数据探索分析\n",
    "数据特征79维，需要分析出数值型和类别型，35个数值型，44个类别型。\n",
    " 1.1导入数据包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#invite people for the Kaggle party\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.2导入训练数据和预测数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把训练数据和测试数据放入同一个文件夹\n",
    "train = pd.read_csv(\"Ames_House_train.csv\")\n",
    "test = pd.read_csv(\"Ames_House_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#显示训练集的前5行\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities      ...       ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0         Lvl    AllPub      ...               120        0    NaN  MnPrv   \n",
       "1         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "2         Lvl    AllPub      ...                 0        0    NaN  MnPrv   \n",
       "3         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "4         HLS    AllPub      ...               144        0    NaN    NaN   \n",
       "\n",
       "  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0      6    2010        WD         Normal  \n",
       "1        Gar2   12500      6    2010        WD         Normal  \n",
       "2         NaN       0      3    2010        WD         Normal  \n",
       "3         NaN       0      6    2010        WD         Normal  \n",
       "4         NaN       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#显示测试集的前5行\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.3 对于目标值的探索分析\n",
    "  SalesPrice 房屋售价，这是要预测的目标变量，首先对它进行分析\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1460.000000\n",
       "mean     180921.195890\n",
       "std       79442.502883\n",
       "min       34900.000000\n",
       "25%      129975.000000\n",
       "50%      163000.000000\n",
       "75%      214000.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#看看目标变量y的描述\n",
    "train['SalePrice'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 180921.20 and sigma = 79415.29\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9beabed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9beabecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#目标变量y的分布情况，如果目标变量y不符合正态分布，利用正态分布函数进行调整\n",
    "sns.distplot(train['SalePrice']);\n",
    "\n",
    "# 得到适合的参数mu和sima\n",
    "(mu, sigma) = norm.fit(train['SalePrice'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#进行绘图，根据y的值和频率绘制y的分布图\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('SalePrice distribution')\n",
    "\n",
    "#绘制y的plot图\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(train['SalePrice'], plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 1.882876\n",
      "Kurtosis: 6.536282\n"
     ]
    }
   ],
   "source": [
    "#偏离和峰值\n",
    "print(\"Skewness: %f\" % train['SalePrice'].skew())\n",
    "print(\"Kurtosis: %f\" % train['SalePrice'].kurt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正态分布属于偏右的右态分布，但是整体峰值向左偏离，skewness较大，需要对目标值做log转换，以恢复目标值的正态性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 12.02 and sigma = 0.40\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9bed65a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9bee618d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用log(1+x) 这个函数，让SalePrice 正态\n",
    "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n",
    "\n",
    "#新生成y的分布图\n",
    "sns.distplot(train['SalePrice'] , fit=norm);\n",
    "\n",
    "# 得到合适的参数\n",
    "(mu, sigma) = norm.fit(train['SalePrice'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#进行绘图，根据y的值和频率绘制y的分布图\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('SalePrice distribution')\n",
    "\n",
    "#得到新的plot图\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(train['SalePrice'], plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 0.121347\n",
      "Kurtosis: 0.809519\n"
     ]
    }
   ],
   "source": [
    "print(\"Skewness: %f\" % train['SalePrice'].skew())\n",
    "print(\"Kurtosis: %f\" % train['SalePrice'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>1453</td>\n",
       "      <td>0.995205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>1406</td>\n",
       "      <td>0.963014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>1369</td>\n",
       "      <td>0.937671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>1179</td>\n",
       "      <td>0.807534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>690</td>\n",
       "      <td>0.472603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>259</td>\n",
       "      <td>0.177397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>38</td>\n",
       "      <td>0.026027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>38</td>\n",
       "      <td>0.026027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>37</td>\n",
       "      <td>0.025342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>37</td>\n",
       "      <td>0.025342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>37</td>\n",
       "      <td>0.025342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>8</td>\n",
       "      <td>0.005479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>8</td>\n",
       "      <td>0.005479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Total   Percent\n",
       "PoolQC         1453  0.995205\n",
       "MiscFeature    1406  0.963014\n",
       "Alley          1369  0.937671\n",
       "Fence          1179  0.807534\n",
       "FireplaceQu     690  0.472603\n",
       "LotFrontage     259  0.177397\n",
       "GarageCond       81  0.055479\n",
       "GarageType       81  0.055479\n",
       "GarageYrBlt      81  0.055479\n",
       "GarageFinish     81  0.055479\n",
       "GarageQual       81  0.055479\n",
       "BsmtExposure     38  0.026027\n",
       "BsmtFinType2     38  0.026027\n",
       "BsmtFinType1     37  0.025342\n",
       "BsmtCond         37  0.025342\n",
       "BsmtQual         37  0.025342\n",
       "MasVnrArea        8  0.005479\n",
       "MasVnrType        8  0.005479\n",
       "Electrical        1  0.000685\n",
       "Utilities         0  0.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = train.isnull().sum().sort_values(ascending=False)\n",
    "percent = (train.isnull().sum()/train.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分离目标特征salePrice # 并将目标值正态化  \n",
    "train_labels = np.log1p(train['SalePrice'])  \n",
    "  \n",
    "# 合并训练集和测试集  \n",
    "all_data = pd.concat((train.loc[:, 'Id': 'SaleCondition'], test.loc[:, 'Id': 'SaleCondition']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'#ID编号对于预测结果无作用，先舍弃暂存以备后用。\\n#先看下训练集的个数和维度\\nprint((\"The origin data train.shape is :{}\").format(train.shape))\\nprint((\"The origin data test.shape is :{}\").format(test.shape))\\n#去掉ID编号，暂存\\nX_ID = train[\\'Id\\']\\ny_ID = test[\\'Id\\']\\nX=train.drop(\\'Id\\',axis=1)\\ny=test.drop(\\'Id\\',axis=1)\\n#查看去掉训练集的个数和维度\\nprint((\"The update data X.shape is :{}\").format(X.shape))\\nprint((\"The update data y.shape is :{}\").format(y.shape))'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''#ID编号对于预测结果无作用，先舍弃暂存以备后用。\n",
    "#先看下训练集的个数和维度\n",
    "print((\"The origin data train.shape is :{}\").format(train.shape))\n",
    "print((\"The origin data test.shape is :{}\").format(test.shape))\n",
    "#去掉ID编号，暂存\n",
    "X_ID = train['Id']\n",
    "y_ID = test['Id']\n",
    "X=train.drop('Id',axis=1)\n",
    "y=test.drop('Id',axis=1)\n",
    "#查看去掉训练集的个数和维度\n",
    "print((\"The update data X.shape is :{}\").format(X.shape))\n",
    "print((\"The update data y.shape is :{}\").format(y.shape))'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train\n",
    "y=test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>2909</td>\n",
       "      <td>0.996574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>2814</td>\n",
       "      <td>0.964029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>2721</td>\n",
       "      <td>0.932169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>2348</td>\n",
       "      <td>0.804385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>1420</td>\n",
       "      <td>0.486468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>486</td>\n",
       "      <td>0.166495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>159</td>\n",
       "      <td>0.054471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>159</td>\n",
       "      <td>0.054471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>159</td>\n",
       "      <td>0.054471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>159</td>\n",
       "      <td>0.054471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>157</td>\n",
       "      <td>0.053786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>82</td>\n",
       "      <td>0.028092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>82</td>\n",
       "      <td>0.028092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>81</td>\n",
       "      <td>0.027749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>80</td>\n",
       "      <td>0.027407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>79</td>\n",
       "      <td>0.027064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>24</td>\n",
       "      <td>0.008222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>23</td>\n",
       "      <td>0.007879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>4</td>\n",
       "      <td>0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Total   Percent\n",
       "PoolQC         2909  0.996574\n",
       "MiscFeature    2814  0.964029\n",
       "Alley          2721  0.932169\n",
       "Fence          2348  0.804385\n",
       "FireplaceQu    1420  0.486468\n",
       "LotFrontage     486  0.166495\n",
       "GarageCond      159  0.054471\n",
       "GarageQual      159  0.054471\n",
       "GarageYrBlt     159  0.054471\n",
       "GarageFinish    159  0.054471\n",
       "GarageType      157  0.053786\n",
       "BsmtCond         82  0.028092\n",
       "BsmtExposure     82  0.028092\n",
       "BsmtQual         81  0.027749\n",
       "BsmtFinType2     80  0.027407\n",
       "BsmtFinType1     79  0.027064\n",
       "MasVnrType       24  0.008222\n",
       "MasVnrArea       23  0.007879\n",
       "MSZoning          4  0.001370\n",
       "BsmtHalfBath      2  0.000685"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total =all_data.isnull().sum().sort_values(ascending=False)\n",
    "percent = (all_data.isnull().sum()/all_data.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、数据清洗\n",
    "主要是针对异常值的处理和缺失值的处理\n",
    "1、对于丢失数据超过90%的进行删除\n",
    "2、对于y的离群点进行删除\n",
    "3、对于相关性很高的也可考虑删除\n",
    "4、对于丢失数据的进行添补"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1对于缺失率大于90%的删除（PoolQC, MiscFeature, Alley），实际是对特征X降维了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data=all_data.drop(['PoolQC','MiscFeature','Alley'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2一般情况下，目标y值会存在一些离群点，在模型训练中要去掉已达到更好的训练效果，如果噪音过大，建议删除，对结果影响不大的就不要删除了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9bef24d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()  \n",
    "ax.scatter(x = X['GrLivArea'], y = X['SalePrice'])  \n",
    "plt.ylabel('SalePrice', fontsize=13)  \n",
    "plt.xlabel('GrLivArea', fontsize=13)  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过这个表格可以看出，右下角的两个点是噪声，面积很大，价格很低，这种应该是很偏的地方，删除对于结果没什么影响。而右上方的两个点，面积大，价格也很大，虽然感觉是离群点，但是对于预测结果还是有些影响，先不删除了，观察下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop(X[(X['GrLivArea'] > 4000) & (X['SalePrice'] < 300000)].index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9bed0ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#删除后再观察分布图\n",
    "fig, ax = plt.subplots()  \n",
    "ax.scatter(x = X['GrLivArea'], y = X['SalePrice'])  \n",
    "plt.ylabel('SalePrice', fontsize=13)  \n",
    "plt.xlabel('GrLivArea', fontsize=13)  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.3针对两两相关的特征X，线性度很高的变量，可考虑删除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j0vZxFLfqARYBwwEkrUfR47plRIxKS7J+mqIBu5qIeBV4WtKHUqwkvS0dXh94Nm2f2PmnYmZmZrZu6+2N1SGS5pVeXwI+B3xU0gzgI8DnU9rLgVMlPQh8CLg1IhpKef0ZOEzSwBrlHAd8XNJDwMPA+9P+iRTDA6YACzr75MzMzGwdE01d++qFevUDVhHRUmP7wBpp72T1qat+XXV8IfCG9HZU1bGngUNq5PlnikaumZmZmXWBXt1YNTMzM1ur9NJxpV3JjdVuVM+Yiz0b8tYkWHjXiuwyntjpK9kx+z6cv+jX98Z+IzvmlB+8NTuGhuXZIasemJUd8+ItK7PS37Nyw+wyPrrrM9kxgw/bPTum6fn52TH79v1ndsxVj22RHfOGrx6UHTP3Uw9mpf/ngPxv58Hfyn9gcpvf3JMd8+ijb2g7UZV7B+Z/B479wIDsmFjW0vOrLfvohMzPwAYbZ5fBqy9nh7z4f/m/m7fv+Gp2zE8e3jw7ZkxDzTVrWjWgjtu9y/rk/Xvzcr/8781bVqzKjnlqQH5TZZNrft12Ius13Fg1MzMz6yncs9pMb3/AyszMzMzWYt3aWJU0UtKfJT0u6UlJZ0vKv/eUV+bi9HOUpFml/XtLuk/SbElzJH26M8oxMzMzq1dEY5e+eqNua6xKEnA1cE1EbAu8FRgG/G8H880eyiDpTRSrXJ0cEdsD7wA+JumIjtTFzMzMrEO8KEAz3dmzeiCwPCJ+AxBF8/6LFI3E+yW9tlSppMmSxkoaKunCdPxBSe9Px0+SdKWkvwB/lzRM0i2SHpA0s5KuFZ8GLoqIB1JdFgCnUSyZiqSLJH2wVJ9K72xuOWZmZmbWAd35gNVOFMuaviYiXpX0L+A64CjgTEkjgM0iYpqk71BM3v8xSRsA90m6OYXvCYyOiIWpd/WIlN8mwD2Sro2Ilh5V3Qm4uGrfVFafh7WW5ZnlmJmZmbVfL524vyt1Z8+qgFqNOgGTKVaVgqLRemXafjdwuqTpKc0gYMt07KY0kX8lj++kVatuBjYHNq2jLu05h5xykDRB0lRJU+9Y/HgdRZqZmZmtu7qzsfowMK68Q9J6wBbA/cBLkkYDR1MsjQpF4/DIiBiTXltGxKPp2JJSVsdRrD41NiLGAC9QNGzbXRdgLEXvKsAq0rVJY20rD4HllkNETIqIcRExbu9h+fMympmZ2TrEY1ab6c7G6i3AEEknAEjqC/yYYuzoUooG6mnA+hExM8XcCHw2NRiRtGsLea8PzI+IlZIOALZqoy7nASdJGpPy3ZjiQa9vpeNzKRqvAO8H+tdZjpmZmZl1QLc1VtO4ziOAD0l6HHiMYgzoV1OSq4BjgD+Uwr5F0VCckaad+ha1XQaMkzSVovdzdht1eQ44HpgkaQ7wb+CciPhHSnI+sJ+k+4A9eL0XN6scMzMzsyzR1LWvXqhbV7CKiGeA97Vw7IXq+kTEMuCTNdJeBFxUer+A4oGrWvkOSz/nAjuX9t8OjAdIc6x+VdINEfFyqsvbS9l8pb3lmJmZmVnnkR9k7z6XbXZ89sV+vhv+O7FxHXMEP9Mv/3Nz+rSWOsZbtutOH86O+RGjsmNuHZy/9vYWjXnraANsvjLvf7V3Dcq/zovIX3t7CPnnsldDfkw9/6d/akD+76aOy8YeK5dlpb9gYH69dmxqdYh7TSPyf5081T//AgyJ/PMZVscv9JL4d3bMDv03yko/tI5+mDEr82OW1XFvcusV+b/QV/rkf9eG1NGDVk85GzXmnc8L/fKv84aN+ecyr3/+L+czz/w2/0vQBZb9/edd2jAb/O5P9YjzzOHlVs26SW5D1bpPbkPVuk9uQ9W6T25D1axe3ToMwMzMzMxa0UvHlXYl96yamZmZWY/V5Y1VSSMl/VnS45KelHS2pAFtR3aozMryqKPSLAKV/eMl3S5pjqTZki6QNKQTypso6ZSO5mNmZmbrOM+z2kyXNlbT/KhXA9dExLbAW4FhFHOadiTf7OELkjalWBnryxGxHbADcAMwvCN1MTMzM7Ou09U9qwcCyyPiNwAR0Qh8EfiYpPsl7VRJKGmypLGShkq6MB1/UNL70/GTJF0p6S/A3yUNk3SLpAckzayka8WngYsj4u5Ul4iIqyLiBUkbSbpG0gxJ96SVtCo9phemuj0l6XOl+n4t9dDeDGzXidfMzMzM1lXuWW2mqxurOwHTyjsi4lXgX8B1wFEAkkYAm0XENOBrwK0RsTtwAPBDSUNT+J7AiRFxIMWCAkdExG4p3Y8rK121YOfqupR8E3gwIkZTLFJwSenY9sDBFHOynimpv6SxFAsY7Ap8ANi9pUIlTZA0VdLUW5c+3kr1zMzMzKxaVzdWBdSaL0zAZOBD6f1RFLfoAd4NnC5pekozCNgyHbspIhaW8viOpBnAzcDmwKZ11nNv4FKAiLgV2FjS+unYXyOiIS0IMD+VsQ/wp4hYmhrf17aUcURMiohxETHuwCHb1lk9MzMzWyd4Batmurqx+jAwrrxD0nrAFsD9wEvplvvRwOWVJMCRETEmvbaMiEfTsSWlrI4D3gCMjYgxwAsUDdvW6jK2hWO1emQrjeyG0r5GXp/uy6spmJmZmXWxrm6s3gIMkXQCgKS+wI+BiyJiKUUD9TRg/YiYmWJuBD5buaUvadcW8l4fmB8RKyUdAGzVRl3OBU6UtEdlh6TjJb0JuJ2i8Yuk/YEFqce0JbcDR0gaLGk4LSwha2ZmZpbFY1ab6dLGahRruR4BfEjS48BjFGNNv5qSXEUx9vMPpbBvAf2BGWnaqZbW6LwMGCdpKkVDc3YbdXkhlfWj9GDUoxS3818FJqa8ZgDfA05sI68HgCuA6cAfgSmtpTczMzOz+nT5ClYR8Qwt9DymBmS/qn3LgE/WSHsRcFHp/QKKB65q5Tss/ZxL8WBVZf/dFA3UakuBZrMJRMTEqvflvP6XDk7BZWZmZraaXjqutCt5udVutLSOfuyRK/OGxo6MhrYTVXmyb2tDfWs75QdvzY7ZdacPZ8c8+PDvsmNWTflD24mqjL/k+uyYZc/3zUp/1bObZZdxXNOi7Ji3fCD/D92yma2NeqnthtlbZMdM6Z//+Txl0OLsmGhqbWKQ5m6K/PXnf/bJvN8/wMKr52XHXDZ/RHbMPK3Ijjlqef4fqA0HLM+O+dj0C7LSr/rLL7PLiAULsmPmX/ZMdszypf2zYw5veDE75lvk/719YkD+53NY5p+Op/rmNyE2bMwO4aV++Z/Nj5+zc9uJrNdwY9XMzMysp+il40q7khurZmZmZj2FhwE009WzAXSICndIOrS07yhJN3RC3r+V9LSk6ZJmS/p6O2KOkHRq2v62pC+k7Y+lWQXMzMzMrBP16J7ViAhJJwNXSroN6EvxUNMhHclXUuW8vxgR10gaDMyWdHF6IKyl+vyphUMfAx4Anu9IvczMzGwd52EAzfTonlWAiJgF/AX4MnAmcElEPCnpREn3pZ7Rn0vqAyBpUlre9GFJZ1TykTRP0jck3UkxnVbZYIpJ/peW0m6Qtt8u6ea0/QlJ/1cOlHQ0MAa4ItVlQFdcBzMzM7N1UY9vrCbfBD4MHAr8QNLOFA3OvdLqVf0o5lAFOD0ixgFvA94lacdSPksi4h0RUVna9adpWddnKBrBL+VWLCIq860enVbcyn8M18zMzAy8KEANvaKxGhFLKCbhvzQiGoB3ArsDU1Njcz9gm5T8WEkPUNyW3wEoN1avqMr6i6mx+ybgPZLGd3bdJU1IPb1Tb1/yeGdnb2ZmZrZW69FjVqs0pReAgAsj4hvlBJK2BT4PjI+I/0j6LVCeRHRJrYwjYpGkfwB7A/cBq3i9IZ8/CenqeU8CJgGcP/L4vElTzczMbN0SbipU6xU9qzXcDBwlaRMASRtL2hJYD1gEvCppBHBwezKT1B8YDzyZds0FxqbtI9uRxSJgeLtrb2ZmZmbt0pt6Vl8TETMlfRO4OT1YtRI4GZgKPALMAp4C7mwjq59KmggMBG4Erk37JwLnS3qeoqe1Lb8BLpC0jKJX1+NWzczMLF8vHVfalXpNYzUiJla9/x1Qay3Oj7QQP7Lq/fGtlDUZ2LbG/gtK218vbf8ByF/j08zMzMxa1Wsaq2ZmZmZrPfesNuPGajeq5+PXP3Og9ZLopl9pw/LskB8xKjtm1ZT8Dut++xyVHfP8KVOyY15dNjArfUP/7CIYNGBldkzDnJrPEbYe82r+52aH2s8rtmp+09DsmI23mp8d89iMTbLSD+mbXQQsa8gOWfyfvM8MwNA6/nAM6pt/Qpuv/2p2zIuvDsmOWTXz1qz0jbOfyC6jaeHi7JhVK/If4Rg0JP/7udeKrbJj+i/Pf+Cmfyg7JvcKDK7jOaA+5Aet35hfDivyv5/Wc7mxamZmZtZThHtWq/XW2QDMzMzMbB3QamNVhTskHVrad5SkGzpasKTfSno6LVH6kKQDOppnZvnflvSF0vsBkhZK+lYrMe+UdE0Lx15botXMzMysLl7BqplWG6sRERRTQv1E0iBJQ4H/BT7dkUIlVYYfVFaQOgX4eUfy7ASHUEx7dfQaroeZmZmZJW0OA4iIWcBfgC8DZwKXRMSTkk6UdF/qGf15mu8USZPS8qIPSzqjkk/qefyGpDuBI6qKuRvYvJR2d0n/kDRN0vWSNk3775D0E0lTJD0iaZykP0l6PM2XWok/TdKs9Ppsaf8ZkuZIuonmU1MdC/wEeEHS7qWY/0oxdwDvL+1/g6SbJD0g6RcUq2qZmZmZ1S+ia1+9UHvHrH4T+DBwKPADSTtTNDj3Sj2j/YBjUtrTI2Ic8DbgXZJ2LOWzJCLeERFXVuV/CHANgKSBwNnAkRExFvgtUL41vywi9gF+nWJOBnYBJkjaQNJ44DiKFan2BD4laXTafyQwBvhgOk4qcyiwH/A34PcUDVckDQF+BbwH2AfYrOqa3BYRuwE3VB0zMzMzs07QrtkAImKJpCuAxRHRIOmdwO7AVEkAg4FnUvJjJX085b0ZsCPF7XWAK6qy/qmknwKb8HrjcQdgJ4rVqQD6AvNKMZVVpmYCMyPiBQBJc4GRFI3KP0bE0rT/GmBvYEjavwxYJukvpTwPA26KiOWSrkzndUqq+2MR8WTK6zLghBSzL0Ujloj4s6RFta6dpAnABIDjNhjPPkObrTVgZmZmVuil40q7Us7UVU28PlWogAsj4hvlBJK2BT5PseTofyT9FhhUSlI9MeMXKYYYfBG4CNgj5T0j9Z7WUpk8ram0XXnfj9Zvx7fU/30ssEdq8AK8kaIxuriVmNbyez1BxCRgEsCvRh7fO/vfzczMzNaQeqeuuhk4StImAJI2lrQlsB6wCHhV0gjg4LYyiohG4MfAEEkHUfTCbp5u21ee0t8po263A0dIGixpGMU40ylp/wfSg2LrAe9N+W9I0UgeGRGjImIU8DmKBuwjwFslvVlFN++xVeUcl/J4HzA8o45mZmZmzXk2gGbqWhQgImZK+ibFrfo+wEqKsaNTKRp4s4CngDvbmV9I+jZwWkTcIumDwDmShqc6/hh4uJ153Sfp98D9adcvImImgKQ/AQ8Bcykam1CMY70pIspLkVxDMevBZ9J5XQ8sSOezXUpzJvB7SUcBtwHPtqd+ZmZmZi3yogDNtLuxGhETq97/DvhdjaQfaSF+ZNX746veX0Ea0xoRD1CMM63OY+/S9s0UPby1jv0A+EGN+LOAs2pU74KqdC9SDAUA+Gt6Vef1IvDO0q7/VyNfMzMzM+sAL7dqZmZm1kNEkx9vqebGajdaWscI4YY+edO3vmVA9TNsbdOyIdkxqx6YlR1z6+Bh2THjL7k+O+b5U6Zkx2x778/yyzn0v7PSN83Pv84LF+XHjNhgWXbM0EGrsmP6z2/Mjqnn5taAkQOyY5ZMz/vTNrCOWZIXTX4+O6Zvv0FtJ6qyy4qGthNV0YCB2TEbvGlpdszQ9fPrtvCrtW7ItWz4jn2zy6jHosWDs2MWr+ifHbPZoPzP87LMfwcAtl6R/51+tn/e92ZlHd+b/soPWl5HOU0z8v+N8pJAPZcbq2ZmZmY9RS99CKor1TsbgJmZmZlZl+uWxqqkkHRp6X0/SS9Kuq6OvCZLOrhq3xck/byOvPpJWiDpu7mxZmZmZp0umrr21QuEA88RAAAgAElEQVR1V8/qEmBnSZVBQe+i/qmefs/rS7tWHJP2t4ukyiCodwNzKOaMrTkqppTWzMzMbK0n6RBJcyQ9Ien0GsdPSp2O09PrE6VjJ0p6PL1O7Iz6dOcwgOuB/0rbx1JqXEoaL+kuSQ+mn9ul/TtJui9diBlphayrgPdKGpjSjKJY1vUOSfunnterJM2WdFmlESpprqQzJN0BfKhUj7OBfwFvL9VntbSStpF0g6RpkqZI2j6le5+ke1O9b5a0aRddOzMzM1sXNEXXvtqQOunOAw6lWHb+WEk71kh6RUSMSa8LUuxGFPPQ7wGMB85Miy91SHc2Vi8HjpE0CBgN3Fs6NhvYNyJ2Bc4AvpP2nwycHRFjgHHAvIh4CbgPOCSlOYbiglV+A7sCX6C4wFsD7yiVszwi9o6Iy1Mv70HAdRQN5/LqVKulpVgu9bMRMRY4BagMObgDeHuq9+XAafVcGDMzM7MeYjzwREQ8FRErKNo3729n7MEUCy0tjIiXgZt4vb1Wt25rrEbEDGAURaPwb1WH1weulDQL+ClQWV71buCrkr4MbBURlTl5ykMBqocA3BcR8yKiCZieyqy4orT9XuC2iFgK/JFiida+1WnTkq17pfpNB34FjEhpRgI3SpoJnFqq92skTZA0VdLUuxc/XvPamJmZmQE9YbnVzYFnSu/npX3Vjkx3va+StEVmbJbung3gWuBHNB9f+i2KhuPOwPuAQfDaKlmHAcsoGoUHpvTXAAdJ2g0YnFa8qihP/NfI6tNzlSchPRZ4p6S5wDRgY+CAGmn7AP8pdXWPiYgd0rGfAedGxC7AJyv1LouISRExLiLG7Tls25oXxczMzKw7lDvR0mtCdZIaYdXjB/4CjIqI0RSriV6cEZutu+dZvRB4JSJmStq/tH99Xn/g6qTKTklbA09FxDlpezRwa0QsljQ55dfuB6tK+a5HsZzrFhHRkPZ9lKIBe3M5bUS8KulpSR+KiCvTGNjREfFQVb07ZRCxmZmZrcO6eJ7ViJhEMbyxJfOALUrvRwL/rsrjpdLb84Hvl2L3r4qdXGdVX9OtPavp9vzZNQ79APiupDuB8q34o4FZ6fb79sAlpWO/B95GMZYi1wcoGr3lXtg/A4dVHtyqchzwcUkPAQ/z+tiNiRTDA6YAC+qoh5mZmVlPcj+wraQ3SxpAMdzy2nICSSNKbw8DHk3bNwLvlrRherDq3Wlfh3RLz2pENFtnMyImk1rbEXE38NbS4W+k/d8Fas6BGhF/oqq7uZxnev+Z0vao0vZFwEVVsQuBN6S3o6qOPU2NAcIR8WeKRq6ZmZlZx0WH75p3sPhYJekzFI3MvsCFEfGwpLOAqRFxLfA5SYcBq4CFpLviEbFQ0rcoGrwAZ6X2VYd4uVUzMzMze01E/I2qh+Ej4ozS9leAr7QQeyHFMM1O48ZqNxpWxzCUf/avuVZBi96yMv9XuvmwxdkxL96yMjtmi8b1s2OWPZ+/JsOry2qN5Gjd84f+d3bMm64/Pyv94N3OaDtRlf0W3p0d88rOB2XHND2XP4rlvAfzr/O2dXQYPHL98OyYfa4+LCv9rR/4S3YZdzye/4Dry33zvs8ATw3O/8MxrI6emRueHJkdc9Bmz2XHvDAv7/e5ctnS7DIGb5D/92n4sOXZMS+8NLjtRFX61Hz+pHX96vje1DPGb0jmR60x/1SoZzTmoDo+z00v5/+71mN08ZjV3qi7ZwMwMzMzM2s396yamZmZ9RTtWGVqXdNtPauSGtOyqQ9JekDSXp2Q5xhJ7ym9r16r9pK0/yxJ72wjr00lXZfq94ikv6X9oyQtK+U5XdIASdtLultSg6RTOnouZmZmZtZcd/asLkvLpiLpYIqn/PfrYJ6VZVjLg4CvKM8CAKsPCm7FWRRLhJ2d6ji6dOzJSt0rJC0EPgccXk/FzczMzJoJj1mttqbGrK4HvAzFXF2Sbk89lrMk7ZP2L5b0fUnTJN0sabykyZKeknRYmvvrLODoFHt0S4VJukjSB9P2XEnfTL27MyVtn5KNoJjMFnhtedgWRcT8iLgfyB/Jb2ZmZlZLU3Ttqxfqzsbq4NSonA1cQLHEKsCHgRtTz+XbgOlp/1BgckSMBRYB3wbeBRxBMW/XCuAMip7UMRFxRYqrNF6np1WpalkQEbsBvwAqt/DPA34t6TZJX5O0WSn9NqU8z+vohTAzMzOz9llTwwD2BC6RtDPFxLEXSuoPXBMRlcbqCuCGtD0TaIiIlZJmUjVpf5VmwwBquDr9nEaxmhURcWNa0vUQ4FDgwVQ/qDEMoL3SmrsTAI7fYDz7Dt22nmzMzMxsHRCeuqqZNTIMIK1YtQnwhoi4HdgXeBa4VNIJKdnKiNcmV2sCGlJsEx1vZFeWWW0s5xURCyPidxHxEYpG9L4dLIeImBQR4yJinBuqZmZmZnnWSGM1jRPtC7wkaStgfkScD/wa2C0jq0VA/ozhtet0oKQhaXs4sA3wr87I28zMzKxdPGa1me4cBjBYUuUWv4ATI6JR0v7AqZJWAouBE1rKoIbbgNNTvt/tYP3GAudKWkXRiL8gIu6XNKpWYklvAqZSPCzWJOkLwI4R8WoH62FmZmZmSbc1ViOi5rqZEXExcHGN/cNK2xNrHYuIhcDuVaEX1cjrpNL2qNL2VGD/tP1D4Ic1YucCO9fY/zyQvz6hmZmZWUs8dVUzXm7VzMzMzHosvf4Mk3W132x+fPbFfjGz73uHhsbcItiojqli7xkwKDvmzSvy/7f4zID8/081KDuEev4fOzjzt/mpB87KLuNjY/MXR/ufFfkXoDHyY24ZODA7ZtuV+eXUcTqM7f9KVvp/LxuaXcb0Qfk3ptar44PWv44/0cvruGY1b321YfOV+Sc0Y2Be5TZsyj+ZgXVcs81XrsqOWdAv/zMwpI4xg/P75V+D7Vbk/11frLxPwSt9u+fv8waN+ddsWZ/8gk549rd11K7zLTnruC5tmA0947IecZ453LNqZmZmZj1Wdz5gZWZmZmat8TyrzXRbz6qkxrQC1ENpqdO9OiHPMZLeU3p/kqQXS6tNXZL2nyXpnW3ktamk61L9HpH0t7R/lKRlpTynSxog6ThJM9LrLklv6+j5mJmZmdnq1tQKVgdTTDW1XwfzHAOMA/5W2tdsBauIOKMdeZ0F3BQRZ6c6ji4da7aClaSngf0i4mVJhwKTgD3qOAczMzOzQi+dC7Urrakxq+sBLwNIGiHp9tRjOUvSPmn/YknflzRN0s2SxkuaLOkpSYdJGkDRwDw6xR7dUmGSLpL0wbQ9V9I3U+/uzLRAAcAIYF4lJiJmtHYCEXFXRLyc3t6Dp7EyMzMz63Td2VgdnBqVs4ELgG+l/R8Gbkw9l28DKgsHDAUmR8RYipWqvg28CzgCOCsiVgBnUPSkjomIK1JcpfE6XdJHW6jLgojYDfgFUHnc+jzg15Juk/Q1SZuV0m9TyvO8Gvl9HLg+73KYmZmZVYmmrn31QmtqGMCewCWSdgbuBy6U1B+4JiIqjdUVwA1peybQEBErJc0ERrVSTrNhADVcnX5OAz4AEBE3StoaOAQ4FHgw1Q9qDAOokHQARWN17xaOTwAmAJyw/nj2H7ptG1UzMzMzs4o1MgwgIu4GNgHeEBG3A/sCzwKXSqost7oyXp8EtgloSLFNdLyR3ZB+NpbzioiFEfG7iPgIRSN639YySeNaLwDeHxEv1UoTEZMiYlxEjHND1czMzFrVFF376oXWSGM1jRPtC7wkaStgfkScD/wa2C0jq0XA8E6q04GShqTt4cA2wL9aSb8lRQ/tRyLisc6og5mZmZmtrjuHAQyWVLnFL+DEiGiUtD9wqqSVwGLghJYyqOE24PSU73c7WL+xwLmSVlE04i+IiPsljWoh/RnAxsDPJQGsiohxHayDmZmZrcPC86w2022N1YiouY5bRFwMXFxj/7DS9sRaxyJiIbB7VehFNfI6qbQ9qrQ9Fdg/bf8Q+GGN2LnAzjX2fwL4RLMTMjMzM7NO4xWszMzMzHqKXjqutCu5sdrDPa9VWekP7Lc8u4yZr3dit9tHd30mO+Y7D23WdqIqxzUtyo4ZNGBldszCRUOyY/ZbeHdW+qljT2k7UZULp/0oO2bFOV/Njlk554XsmKfv2Dw7ZkDk/xE+YNS/s2PePntBVvqPDR/ddqIqn9k+/zvw+0e2yI7Zp/8r2TGzl6+XHfOON+df5/ufGpEdc9TghVnp+/dvzC5jRUP+P213L9koO2bHWJod82if/L81Gzbmf2/m9u+fHdOgvPT1PPSyyar8c3muX2bFgPdtnP83rcdwY7WZNbUogJmZmZlZm9yzamZmZtZT9NKJ+7vSGutZldSYVoR6KC19ulcn5DlG0ntK7ydKOqUqzVxJm7SRz/apbg9K2iataPWwpBlp/x4p3WRJc0qrW32wo+dgZmZmZq9bkz2r5RWtDqaYemq/DuY5BhgH/K2D+RwO/Dkizkyrbb0X2C0iGlJDd0Ap7XFpVgEzMzOzjvGY1WZ6ypjV9YCXASSNkHR76qmcJWmftH+xpO9LmibpZknjU8/mU5IOkzQAOAs4OsUe3VqBkkZJelTS+anX9O+SBqee2S8An5B0GzACWBARlRW0FkRE/pMIZmZmZpZtTTZWB6dG5WyKJUu/lfZ/GLgx9bq+DagsJDAUmBwRYylWrvo28C7gCOCsiFhBMVH/FRExJiKuaEcdtgXOi4idgP8AR0bE34BfAj+NiAOAvwNbSHpM0s8lVff+XlYaBrBxfZfCzMzMDKIpuvTVG63Jxuqy1KjcHjgEuETFUlD3Ax+VNBHYJSIqcxetAG5I2zOBf0TEyrQ9qoUyWvqtVPY/HRGVxvC0WvlExGKK1a0mAC8CV0g6qZTkuHQeYyLipep4SRMkTZU0dfKSx1uojpmZmZnV0iOGAUTE3cAmwBsi4nZgX+BZ4FJJleVXV0a8NkljE1C5Ld9Ey2NvXwI2rNo3nKIXlUoeSWNL+UREY0RMjogzgc8AR2ac26SIGBcR4/Yfum17w8zMzGxd1BRd++qFekRjVdL2QF/gJUlbAfMj4nzg18BuGVktomiMVtwOHCZpeCrnA8BDEdHuWaYlbSep3MocA/wzo05mZmZmVqc1ORvAYEmVW/ACToyIRkn7A6dKWgksBk5oKYMabgNOT/l+NyKukHQucIekAOYDn8is5zDgZ5I2AFYBT1AMCTAzMzPrXE2eZ7XaGmusRkTfFvZfDFxcY/+w0vbEWsciYiGwe9WxXwG/qpHfXGDn0vsflbYnlranATXngI2I/WvtNzMzM7PO4RWszMzMzHqKXjqutCu5sdqNGuoYIfxk0+Ks9FvvvqjtRFU2eXpJdszgw3ZvO1GVRQ/lD/V9ywfyb4c0zMk/nxEbLMuOeWXng7LSP3Rufr1WnPPV7JgBn/tOfkx2BPx17JeyY/ZrGtZ2oirDxw3Njnlww7zPzbXTlV1G32H5MUvq+RuwbHjbiao8PTC/bnvXUbd931HHlNOZ5QzYZfPsIjQw/xM9+IpnsmOaGvOv843/GZwds3NDfuNl08aV2THz+uVdt4b8029xip7WDKkjaNOftjrVuvUybqyamZmZ9RTuWW2mR8wGYGZmZmZWS5c1ViVtXFrZ6XlJz5beN7vXIGkjSSe3I99+kv6Ttt8iaVnK8yFJd1ZNM1Vv3Q+U9PbS+x0k/SOV86ikX6T975T0Sum8buxo2WZmZrbuioguffVGXTYMIK3mNAYgrUa1uPzEfQ0bASdTLHWaY05amhVJnwZOBz6eXeHVHQgsAO5J788FfhARf02rbO1cSntbRBzewfLMzMzMrIY1MgxA0mmSZqXXZ9Pu7wHbpR7K70laT9Ktkh6QNEPSe9uR9XrAy6mMXSTdn/KbIWnr1BM7S9KFkh6WdImkgyXdJekxSeMkbUMxF+upKXYvYAQwDyAKMzv/qpiZmdk6zytYNdPtD1hJGg8cB4ynWLXqPkn/oOgRfUupl7Q/8P6IWCTpjcCdwHU1stwuLQKwHjAQ2CPt/xTwo7QwwECKhQdGAtsBRwGzgQeAhojYS9KRwOkR8UFJFwALIuL/Ul1+Atwu6U7g78BvIuKVVM4BpcUNLo+I73XKhTIzMzOzNdKzug/wx4hYGhGLgGuAvWukE/B9STMoGohbSNqkRro5ETEmIrYGTuP1YQR3AV+XdBqwRUQsT/ufiIhHIqIJeAS4Oe2fCYyqVeGIuADYEbgKOAi4uzTu9rZU/phaDVVJEyRNlTR1yuLHW74qZmZmZu5ZbWZNNFbbOzPbCcD6wG6pt3UBMKiNmGuBfQEi4lLgCKABuEnSvilNQyl9U+l9E630NEfEsxFxYUS8j+K67dCek4iISRExLiLG7TOsw89+mZmZma1T1kRj9XbgCEmDJQ0D3g9MARYB5dmv1wfmR8QqSe8C2jMz9N7AkwCSto6IJyLibOCvwOiMOq5WF0mHSOqXtjcDNgTqmA3bzMzMrGXRFF366o26fcxqRNwn6ffA/WnXLyoPLKXb5TMpGpc/Af4iaSrF2NKW7qFXxqyKopd0Qtr/YUnHAispGpZfB2oNI6jlz8CVkj4AfBo4FDhb0nKKBTi+EBEvFhMDmJmZmXWSXtqg7Erd0liNiIlV738A/KBGuur10faoTpNskNI/AdRcuy4ivg18u2r3f0jTaaU0x5e2n6gci4jZwC6luLtaKONmXh/zamZmZmadzMutdqNB+cvc09g3739YJ0wbxu8/kDe6Y/DLi7LSAzQ9Pz87Zgh9s2OWzXw1O6bh1fyP9dBBq7Jjmp5bkJV+lyNh+lVDsmJWznkhKz1A/qro9Vke+ddsYB0dBn13zh/rveSuGVnpDxrxHLc8NyIrpu8mA7PSA7yg/Gu2f58V2TGDlud/CoaNyV+znn753+mFty/LSr/JTnX84eyTf9dLffI/nMsX5V/nIZFft4GRfw36kH8+gzN79Jb1zT+X/nVNSl/HXczlS+sop4eo4yO/tvNyq2uZ3IaqdZ/chqp1n9yGqpmZdR/3rJqZmZn1EL31Iaiu5G44MzMzM+uxemVjNS2XOl/SrDbS7Z+WS628nyjp2bSM6nRJ30v7J0sa10Ie75X0oKSHJD0i6ZOt5WVmZmZWNy8K0ExvHQZwEXAucEkb6fYHFrP60/w/jYgftaeQtEzrJGB8RMxL70fVk5eZmZmZ5euVPasRcTuwsLxP0udSz+cMSZdLGgWcDHwx9Xzu0568JS2WdJakeymmzuoHvJTKbYiIOZ15LmZmZmavaeriVy/UKxurLTgd2DUiRgMnR8Rc4JcUvZ9jImJKSvfF0q37g2vkMxSYFRF7pEbxtcA/Jf1e0nGSytesrbyQNCEtdjB18pKW1jUwMzMzs1rWpsbqDOAySccDrU1mWGm8jomIG2scbwT+WHkTEZ8ADgLuA04BLszIi4iYFBHjImLc/kPz54s0MzOzdYeXW21ubWqs/hdwHjAWmCap3vG4yyOisbwjImZGxE+BdwFHdqyaZmZmZtZea0VjNd2a3yIibgNOo1iOdRiwCBjegXyHSdq/tGsM8M8OVNXMzMysZR6z2kyvnA1A0u8pnvTfRNI84FvARyStT7Eu208j4j+S/gJcJen9wGfrKQo4TdKvgGXAEuCkTjgFMzMzM2uHXtlYjYhja+z+VY10jwGjS7umVKdJ6fYvbQ8rbS8C3tNCzMT21dbMzMysfXrruNKu1Csbq71VPWMuLtl1cVb6Wy/fPLuMlVovO2bfvvmjIfZqGJkdc8PsLbJjdmBJdkz/+Y1tJ6py3oMDs9JvPnBAdhlP35H/+/zr2C9lxyyP1p5JrO2qB87Jjrlpp69mx/QZu192zP8umJeV/m11fDn7DB+UHfOd896eHRNPPZYdc9u3X86O6Xfou7Nj5n7h79kxT7+6SVb6UZe+ml3Gq8sasmMWNebVC2CrjV7Jjtnx1fzv2r8G5P9T/caVyo7JjRhWxy3leu5C13EqLP35H9tOVGXwoZ/LL8i6hRurZmZmZj1FLx1X2pXWigeszMzMzGzt1Csaq5K2kHSbpEclPSzp85nxkyWNS9tzJc0sTea/l6RRkma1ENtH0jmSZqW4+yW9uaW8On62ZmZmtq6Kpq599Ua9ZRjAKuD/RcQDkoZTzKN6U0Q8Umd+B0TEgsqbtDRrM2mu1g8BmwGjI6JJ0khYbVDkanmZmZmZWefpFY3ViHgOeC5tL5L0KLC5pJ8D9wIHUMyt+vGImCJpMPAbYEfgUWBwe8uSdBLFAgODKJZevQ54LqL4/0hE5D25YWZmZtZevbT3syv1isZqWeoF3ZWikQrQLyLGS3oPcCbwTuB/gKURMVrSaOCBqmxuk9QINETEHjWK2ZOiJ3Vh6km9Q9I+wC3AbyPiwYy8zMzMzNqlt96q70q9YsxqhaRhwB+BL0REZT6Tq9PPacCotL0v8FuAiJgBzKjK6oCIGNNK4/KmiFiY4ucB2wFfofj/zi2SDmpvXpImSJoqaeptSx5v76mamZmZGb2oZ1VSf4qG6mURcXXpUGVCvUZWP5+OzKq72kSdEdEAXA9cL+kF4HCKXtY2RcQkYBLAJZsf75l+zczMrGXuWW2mV/SsShLwa+DRiPhJO0JuB45LsTuz+ipWuWXvJmmztN0n5ZU/I76ZmZmZZestPavvAD4CzJQ0Pe1rbSmcXwC/kTQDmA7c14Gy3wicL6myXNF9wLkdyM/MzMysJo9Zba5XNFYj4g5qrwT3t1KaBaQxqxGxDDimhbxG1dg3F9g5bV8EXFQ6dgNwQ3vzMjMzM7PO0ysaq2ZmZmbrAvesNqcIP/PTXX62Rf4DVluszPvU1vMZX9w3f+jy4jpGO2+8Kv+zdvPAFdkx2zUNbDtRlaZa/fadbPOV+TED6vh+vtgv/2QG1vFnYMTKVdkx73r4O9kxM3f9YnbMFA3PSr9eY3YRNW/1tGV5Hd+bnVYuz475Z59B2THbaUnbiao8oKHZMSszL9zCPvkfzkF1/HbWq+OP5/qN+XVbrzG/oPn9+mbHDGnKr9v6TXlfhOf65fd3rajji9NYR0w9/xZ+5pnfdsO/BG2bf9B+Xdowe+Mt/+gR55nDPatmZmZmPYR7VpvrFbMBmJmZmdm6qUONVUmLO6siKb/DJc2QNFvSLEkf7EBeoyTNStv7S3pF0vT0ujntP1nSCW3kM0TSZZJmpjrdkRYnQFJjKc/paXUtMzMzs/qEuvbVC/WYYQCS3gb8CHhXRDwt6c3AzZKejohpnVDElIh4b3lHRPyyHXGfB16IiF1SPbcDKqMPl0XEmE6om5mZmZnV0OnDACRtJemW1EN6i6QtJfWV9JQKG0hqkrRvSj9F0luAU4DvRMTTAOnnd4D/l9JNljQubW8iaW7aHpXyeCC99sqo60RJp5Ty/76k+yQ9JmmflGwE8GwlJiLmpBWtzMzMzDpVNHXtqzfqijGr5wKXRMRo4DLgnIhoBB4DdgT2BqYB+6SJ9kdGxBPATml/2dQU05r5FL2xuwFHA+e0kG6f0u36r7WQpl9EjAe+AJyZ9l0IfFnS3ZK+LWnbUvrBpTz/1EY9zczMzCxTVzRW9wR+l7YvpWicAkwB9k2v76b9uwP3p+MCqqdraM/giv4UK0zNBK6k5cbtlIgYk17/20Kaq9PPaby+wMB0YGvgh8BGwP2SdkjplpXyPKJWhpImSJoqaeqdix9vx+mYmZnZuiqa1KWv9pB0iKQ5kp6QdHqN41+S9EjpLvpWpWPl53mu7Yxr0h2zAVQaoFOAfYDxFCtPbQDsD9yejj8MjKuK3Y2idxVgFa/XtzyJ4BeBF4C3pfgBHahr5fZ+I6XxvBGxOCKujohPAb8F3tPeDCNiUkSMi4hx7xi2bdsBZmZmZmuIpL7AecChFB2Ax0qq7gh8EBiX7qJfBfygdKzckXdYZ9SpKxqrd/H6UqfHAXek7XuBvYCmiFgOTAc+SdGIheLhqq9UnqhPP79A0aMJMBcYm7bLswSsDzwXEU3AR4D82ZNbIekdkjZM2wMofnH/7MwyzMzMzKBHjFkdDzwREU9FxArgcuD9q9Ux4raIWJre3gOM7MxrUK2jjdUhkuaVXl8CPgd8VNIMisbj5wHSQ0nPUJwUFI3U4cDMdHw68GXgL5Ieoxjj+j8RMSel/xHwP5LuAjYp1eHnwImS7gHeCuQvw9K6bYB/pGEGD1L09P6xk8swMzMz63Ll4YnpNaEqyeYU7bWKeWlfSz4OXF96Pyjle4+kwzujzh2auioiWmrsHthC+n1K27/j9bGtlX1Xk8aNSvoe8G1JB0fEioiYDYwuJf96inm8av9X0v65wM5pezIwuUZ9Jpa29y9tL+D1MauXAJe0cD7Dau03MzMzq0d08VyoETEJmNRKkloVqLkErKTjKYZg7lfavWVE/FvS1sCtkmZGxJN1V5geNM9qtYhoNqDXzMzMzLrUPGCL0vuRwL+rE0n6/+zdd7xdRbn/8c83vZHQESEQhFADBAiR3owKFkRBI4JS1FjgovjDgiI3ioj1chFssRBQgYgUKVdASuiQhJBKCyQoTSAEQkJ6zvP7Y82GlZ19yuycczgn+b557VfWnj3Pmtn7FObMmjXPCOA7wMHlLT0j4vn072xJ44E9gLVzsLo2Wq+O/c36ZG6KNnzES9ltvP54/l9xm3z7Pdkx/3v6I9kxZ/TKT5K20db5n0GPLfPvy3vkH+tl1X9UfbLbOHTQar8fmrXesL7ZMV2H5N/812Wvg5uvVGX6Hqdnx+z68PnZMY/udnZW/fl1rHQ/aexB2TErb7kpO6brjttlx3Dm7OyQoTN+nh2z86++mx0z99oXs+pvcuxW2W3E0mXZMa9en/+z1nvTldkxNz00sPlKVfo01JzUatKWdWwH/hrds+rXM/83cPmK7Jine+QPVT5/00nZMR1FB9gLdSIwOCVneo7iPqRPlStI2gP4LXB4RLxUKt8AWBQRSyVtDOzPqmy5hQYAACAASURBVDdf1cWDVTMzM7MOoqXbS7VZ+xErJJ0K3Exx0/ofI2KmpO8DkyLiOoqb3/sBV0oC+He6838n4LeSGijui/pRROTPVFXxYNXMzMzM3hQR/0exzWi57OzS8YhG4u4Ddm3t/rTHPqtZJG0m6bKUnvWhlDlqtQ33U5rVGTXKv5/WUTTXzh6SQtL7W6vvZmZmZmsiom0fnVGHGqyqmEu+FrgrIt4VEXtRrJXYsqpeozPCEXF2RNzaguaOpdgD9tjG+iKpQ30+ZmZmZuuajjYYOwxYFhG/qRRExL8i4kJJJ0q6UtL1wC2NnUDSWEnHSDpC0l9L5Yek2Mqg+BjgROB9knql8kGSHpX0K2AyMFDS+9Ls7uTUfr9U92xJEyXNkDQmndPMzMysbh0h3WpH09EGq7tQDBIbsy9wQkTU3Me1yj+BfSRVbo0eCYxLx/sDc9K+X+NZNX3qDsClEbEHRYKBs4AREVFJ/fq1VO+iiNg7IoYAvYEPtaBPZmZmZpahow1WVyHpl5KmSpqYiv4ZEfNaEhsRK4CbgA+nZQMfBP6eXj6WIn0Y6d/yUoB/RUQly9Y+FOlV75U0BTgB2Dq9dqikB1Nmq8MoBtq13sObmSLGvzGrJV03MzOzdZRnVlfX0XYDmAkcXXkSEaekfbompaLcVKrjgFOAecDEiFggqWtq40hJ36HYKm4jSZVNM8ttiGKAvMq61rRs4FfAsIh4RtJooFetDpQzRYzd4vhOurTZzMzM7O3R0WZWb6fIKfulUln+TupvGQ/sCXyet5YAjACmRsTAiBgUEVsDVwG18tc+AOwvaTsASX0kbc9bA9O5aQ3rMWvQRzMzMzPAuwHU0qEGqxERFIPGgyXNkTQBuAT4ZiMhO0h6tvT4eNX5VgI3AEekf6G45H9N1Xmuoio7Q4p/meImrMslTaMYvO4YEa8BvwOmU+xeMLE61szMzMzWXEdbBkBEvECxXVUtY0v1noaaueGurDrfqcCppecn1mjzOuC69HRI1Wu3A3vXiDmL4uYrMzMzs1bRWdeVtqUONbNqZmZmZlam6KwLGDqhem6wyv1rYtMVK3KbYLOei7NjFizrkR3zUI+e2TFH9GrR5g+rmLegd3bMGw35FxkOvPrIrPpPHHdF85WqfOj1p7NjHt53w+yYN17Mf//nzs1vZ+eG/O+BjfO/pfnEtO9n1f/GsG9nt7F55H9mfRqyQ5jVNf8D2G5lft+W1jGZc+CKRdkxub8E65lj6ttrWXZMF+X/v/CpN/pnx8zu0TU7ZsDK7BB6N+S/n9yevdI1/6sTdXxBt1qW/zOw61YvZ8cMnHhbh5jSfGrI+9t0YLbtjJs7xPvM4ZlVMzMzM+uwOtyaVTMzM7N1VdRxBWZt12ozq5JWSpqSNvGfLGm/VjjnUEkfKD0/UdLLqZ0pki5tJv4QSTeUYi9Kx6MlPZfO8ZikX0tq8rOQdJSknUvPx0satmbv0MzMzMya0prLABZHxNCI2B04EzivFc45lFVToQKMS+0MjYjPrMG5z4+IoRQZqnYFDm6m/lGprpmZmVmbaAi16aMzaqs1q/2BVwEkbS7prjSLOUPSgal8oaQfS3pI0q2ShqfZytmSjpTUA/g+MDLFjmyssfIsp6SNJT2d0dceFJv8V/r7eUkT0wzxVSkRwH7AkcBPU1+2TbEflzRB0hOV92VmZmZmrac1B6u9K5fVgd8D56TyTwE3p1nM3YEpqbwvMD4i9gIWAD8A3gt8FPh+RCwDzuatmdRKBqrK4HWKpJPWoL+nS5oCvAA8ERGVfl0dEXunGeJHgc9GxH0U+7B+PfXlqVS3W0QMB74K/Pca9MXMzMyMCLXpozNqi2UAOwKHA5dKEkV2p5MkjQZ2jYgFqf4y4KZ0PB24MyKWp+NBTbRTXgZw8Rr0t7IMYFOgr6RKIoIhku6WNB04DtiliXNcnf59qLE+SxolaZKkSePfmLUG3TUzMzNb97TJMoCIuB/YGNgkIu4CDgKeA/4kqbLOdHm8tclrA7A0xTaQv0vBCt56L70y+7qcYtB8UCoaC5waEbsC32vmfEvTvytppM8RMSYihkXEsEP6Ds7pmpmZma1jokFt+uiM2mSwKmlHiv2FX5G0NfBSRPwO+AOwZ8apFgDrtaDe08Be6fiYjPOTZn/3AyqX9tcDXpDUnWJmNbcvZmZmZnWJaNtHZ9QWa1anAOOAEyJiJXAIMEXSw8DRwAUZ57wD2Lm5G6yAnwFfknQfxYxuS1TWrM6gmBX9VSr/LvAg8E/gsVL9K4CvS3q4dIOVmZmZmbWhVksKEBE1M7VFxCXAJTXK+5WOR9d6LSLmAXtXhY6tca7HgN1KRWel8vHA+HQ8thKb2lulzdK5fg38ukb5vay6ddUhpdfm0vQ6WzMzM7NmddZL9W3J6VbNzMzMrMNSdNYFDJ3Qb7c8PvvD7pkZsbKOP8g2WpGf2+1fPfL/ztl7+eLsmIe69c6O6VPHt3TPOtLbbbhyRVb9ev4yfKBXzQsWTdp6Rfv8Vb64jjfUu47PuZ52ZnXN+9r8ZNIPs9u4YM+zs2MG1PH+6/n5fL57/odWz2W2DVbk/7DN7pH3/blBO6We3LiO9zK3W/7PWj2/a9ZryO9bPf9nz+3aoi7577+e9/Jc9/x2+tTxOX/+2T93iCnNGe/6UJsOzIbMvqFDvM8cnlk1MzMzsw6r1dasmpmZmdma6awb97elNZ5ZlbQy3a0/VdLklJp0Tc85VNIHSs9PlHRRVZ03U6w2cZ5yGtaPS3pU0h2SDpE0P/V7Wkr3umlmn0ZLOqO+d2hmZmZmLdEaywAqmat2B84EzmuFcw4FPtBsrTyfBb4cEYem53enfu9GkWXrlLehT2ZmZmZv8j6rq2vtNav9gVcBJG0u6a40ezlD0oGpfKGkH0t6KM1oDk8zoLMlHSmpB/B9YGQL9lclnfPXKaXpTEnfq/H62cABwG8k/bTqNVFs9l/p93BJ96X9VO+TtEMTfdq51PfT6v3QzMzMzKy21liz2jttrt8L2Bw4LJV/Crg5Is6V1BXok8r7AuMj4puSrgF+ALyXYg/TSyLiujS4HBYRp0KxDIBioHhAqd3tSsffiYh5qZ3bJO0WEdMqL0bE9yUdBpwREZMkHQIcmPq9EfAG8O1U/THgoIhYIWkE8MOIOLpGn0YDOwKHUgx2H5f065S+1czMzCxbg9esrqY1lwHsCBwOXJpmKycCJ6VB3a4RsSDVXwbclI6nA3emAd50mt5Yf1xqZ2hEDAUmlV77hKTJwMPALqy6eX9jKssABgIXAz9J5QOAKyXNAM5P52vMjRGxNCUFeAnYrLqCpFFp1nfS3W/MakG3zMzMzKyiVZcBRMT9FOlON4mIu4CDgOeAP0n6TKq2PN7a3LUBWJpiG6hjplfSNsAZwHvS+tMbKWZ5c1yX+gpwDnBHRAwBPtzMuZaWjldSo/8RMSYihkXEsAP7Ds7slpmZma1LItSmj86oVQerknYEugKvSNoaeCkifgf8Adgz41QLKC6tt0R/isv48yVtBhyR0U7FAcBT6XgAxQAb4MQ6+2RmZmZmraA116wCCDghIlamdaFfl7QcWAh8prET1HAH8K103iZ3F4iIqZIeBmYCs4F7W9hGZc2qgPnA51L5T4BLJH0NuL2ePpmZmZnVo7Pesd+W1niwGhE180FGxCXAJTXK+5WOR9d6LSLmAXtXhY6tqntI6fjERvpwSCPH4ylmUGvF3A9sXyr6bhN9KscNaew1MzMzM6uPM1iZmZmZdRDeDWB1Hqy2o951TO3vxBtZ9dfrs7T5SlXmLeidHfP+c/JvFjvlv5/MjrnwCzUn7pu2OP8zWDD+P9kx98zaIqv+kz3yfwGduuMz2TFd++W303XjntkxXdbLvY8R/nZ5/rLvk8Ye1HylKheefFdW/Qv2PDu7ja9M/n52zOTd8pPe7fTBBc1XqvL4Tf2ar1RlyHk7ZsfMvXBCdsyHR26TVT+W5+8G2PDivOyYZ29cmR0zYJPF2TG/fH7z7Jg9lub/TC/skh/TK/P684o6xlQ9oyE7pk9D/v8Hjr/QFzvXJh6smpmZmXUQnfWO/bbU2hmszMzMzMxaTacbrEpamVKeVh6Dmqn/tKSN0/HC9O8gSYtT/NRKWtVmzjNI0qdKz0+UdNGavyMzMzOzQkOoTR+dUacbrPJWxqzK4+k6z/NUit+dYteCbzdTfxBFClkzMzMzayedcbC6mupZTkk3pH1eW6o/8GqKHSTpbkmT02O/VOdHpL1ZJZ2eyt4p6SZJsyT9pNaJzczMzFoq2vjRGXXGG6zKSQjmRMRH6zzPtuk86wF9gHen8peA90bEEkmDgcuBYcC3gDMi4kNQDJCBocAeFGlXH5d0YUTk375tZmZmhreuqqUzDlYXR8TQVjjPU5XzSBoJjAEOB7oDF0kaCqxk1QQB1W6LiPnpHI8AWwOrDFYljQJGAZw4YDiH9s3f8snMzMxsXbVWLAMAVrDqe8ndAPI6oLKZ4+nAi8DuFDOqPZqIK2/ouZIag/+IGBMRwyJimAeqZmZm1pQItemjM1pbBqtPA0MldZE0EBieGX8A8FQ6HgC8EBENwKeBym7ECyiWDJiZmZlZO+mMywBquReYA0wHZgCTWxBTWbMqYBnwuVT+K+AqSR8H7oA3U0hNA1ZImgqMJd2QZWZmZtZa8nN8rf063WA1IlbLIxgRARzXSP1B1bFpu6uaOUYjYhawW6nozFS+HHhPVfWxpbgPtaD7ZmZmZpah0w1WzczMzNZWQedcV9qWPFjt4Pr2WpZVf8aCDbLbWNQl/wdj24sfyI7ZuWHL7Jh5Vz+bHbPwtZ7ZMV275d6TB692zfvc+tdxbefyRwZmx7xRx0r0F7UiO+aHv9wnO2bJuEnZMStvuSk7pk9Dn6z6PevYfHDybmdkx+w57WfZMc+N+EJ2zB3d+mbHDP5H/s/04oX5P2vP/iHvZ7p7j5XZbcx/reaFsya9vjT/M+vaLf+HumcdN7gsqON39OYr8n+m/9Mtb0jQvY6fm7lduzZfqUo9N9csHvuP7JjeH/1WHS1Ze/Bg1czMzKyDaOisO/e3obVlNwAzMzMzWwt1msGqpIVVz1dJsdpIzJt1JG0i6UFJD0s6UNLTkqan9KnTJX2kBX34dul4kKQZ9b4fMzMzs2oNqE0fnVGnGay2gvcAj0XEHhFxdyo7NGWxOgb4RQvO8e3mq5iZmZlZa1krBquSPlyaNb1V0mZVrw8FfgJ8IM2kVq++709p31RJ10p6SNLMlC4VST8Ceqf4v6SqXSX9LtW7pcZ5zczMzFosUJs+OqPONFitDBSnpM38v1967R5gn4jYA7gC+EY5MCKmAGcD4yJiaEQsTi/dkS7l3wmcVQo5OSL2oki3epqkjSLiW8DiFF/Z03Uw8MuI2AV4DTi6dd+ymZmZ2bqtM+0GsDhdsgeK9agUg0mALYFxkjYHelBks2qJQyNirqRtgdskjY+IhRQD1I+mOgMpBqWv1IifkwbCAA8Bg6orpJnZUQAnDhjOoX0Ht7BrZmZmtq5xBqvVdaaZ1aZcCFwUEbsCXwCyNs2MiKeAF4GdJR0CjAD2jYjdgYebON/S0vFKagz+I2JMRAyLiGEeqJqZmZnlWVsGqwOA59LxCbnBkjYFtgH+lc71akQskrQjUN75fLmk7mvaWTMzM7NavGZ1dWvLYHU0cKWku4G5GXF3pPWvdwDfiogXgZuAbpKmAecA5bQuY4BppRuszMzMzKwNdZo1qxHRr+r5WGBsOv478PcaMeU6bx6n54MaaWcpcEQjr30T+GapaEjptfw8imZmZmYlXrO6uk4zWF0b9Kkjh9rdSzfIC+gKR22Xl3v7qcc3ymsDePTRTbJjNs9PCc1fXto8O6ZvHT/puy5b2nylKrN75zf0rhV5FzMO7D4/u42nFq+XHXNIl2XZMTH7ieyYXZYvyY7puuN22TGzuv47O+bgJXlfm50+uCC7jedGfCE7Zotbf5sd87GDvpQd0+v0L2fH/OfIv2bHPNCzZ1b9PeYtz25jWR0XDZcr//Loinn9s2OGKP//Ay91z+/bEuV/BpusWJlV/9Wu+b/U+zXk/958tnv+e5k3K+vWFQA2zI6w9uLB6lomd6Bq7Sd3oGrtJ3egambWVjyzujoPVs3MzMw6iM56E1Rb8nSCmZmZmXVYLRqsSlqZMkdNlTRZ0n45jUgaLemM+rpYP0l7SApJ7y+VDUpZq3LO00/SryU9lVK6PiTp863fYzMzM1uXNahtH51RS2dWK2lGdwfOBM5rjcYltfUyhGMpUrEeu4bn+T3wKjA4pXQ9nBprsSXVcQuRmZmZmTWmnmUA/SkGbgBI+rqkiZKmSfpeqfw7kh6XdCuwQ6l8vKQfSroT+IqkrSXdluJvk7RVqtdY+dg0y3mHpNmSDpb0R0mPShpbakfAMcCJwPsklW8N7CbpknTuv0nqI+kISX8txR8i6fqUinU4cFZENABExMsR8eNSvTskXQZMr+PzNDMzMwOgAbXpozNq6WC1d1oG8BjFLOM5AJLeBwymGMwNBfaSdJCkvYBPAnsAHwP2rjrf+hFxcET8HLgIuDQidgP+Avwi1WmsHGAD4DDgdOB64HxgF2BXSUNTnf2BOSmV6njgA6X4HYAx6dyvA18G/gnsI6lvqjMSGJfOO7UyUG3EcOA7EbFz9QuSRkmaJGnSPxc92cQpzMzMzKxa7jKAHSkugV+aZi7flx4PA5OBHSkGrwcC10TEooh4Hbiu6nzjSsf7Apel4z8BBzRTDnB9RATFTOaLETE9DSZnAoNSnWOBK9LxFay6FOCZiLg3Hf8ZOCAiVlBkr/pwWp7wQWokGkgzxlMkPV8qnhARc6rrAkTEmIgYFhHD3tsnf79IMzMzW3dEGz86o+w1oxFxv6SNgU0AAedFxCq7Vkv6Kk1/Jm801UQLyis7uDeUjivPu6W1o0cDR0r6TurnRpIqu6VXt1F5Pg44BZgHTIyIBZIeAXaX1CUiGiLiXOBcSQtb+H7MzMzMrE7Za1Yl7Qh0BV4BbgZOltQvvbaFpE2Bu4CPSuqdBogfbuKU91EsGQA4juKGqKbKW2IExaX7gRExKCK2Bq4CjkqvbyVp33RcuQkLiuUCewKfJ83+RsSTwCTgB5UbqNL618658MPMzMw6rIY2fnRGLZ1Z7S1pSjoWcEJErARukbQTcH+xKoCFwPERMVnSOGAK8C/g7ibOfRrwR0lfB14GTmqmvCWOBa6pKrsK+FLqy6PACZJ+C8wCfg0QESsl3UBxU9YJpdjPAT8FnpQ0D1gMfDOjP2ZmZmZWhxYNViOi0S2ZIuIC4IIa5ecC59YoP6Tq+dMUN0tV12us/MSqOkNqvPa3GnHX8dba2dVuhCrVOxU4tarsdaBmUu+IGE8xI2tmZma2RhrkC7fVnG61HS3qkv8N+NEdnsmqf+NjA7PbWFHH7rAP9lySHbN15C/tflbLsmN6dc1/Q+rRMzumX+b7WVLH75/HlvTPjpnTM7+hXkt6ZMfc8YNXm69U5dWuvZqvVO3M2dkh23XPa+f57tlN8PhN/bJj7ujWt/lKVT520JeyY7a+69fZMXOP+mx2zI293pEdAyuzas/omf/FyWuhsFj5v596kv9z865l7XOLy5we+TtT9s+8RvyO5fmf9MIu+f3abEX+Z/bLRRtkx5yfHWHtxYNVMzMzsw6is96x35bqSQpgZmZmZtYuWmWwWt7GSdIHJM2StJWkL0r6TCo/UdI7mznPiZIuao0+lc75d0n3V5WNlXRM5nkOlzRB0mNpn9VxlaxaZmZmZq3BuwGsrlWXAUh6D3Ah8L6I+Dfwm9LLJwIzgOdrhLYJSetTbEW1UNI2jW3c34LzDKF4X0dGxKOp7EiKBAT/rqrbLSUYMDMzM7M11GrLACQdCPwO+GBKcYqk0ZLOSLOYw4C/pFnJ3pL2lnSfpKlpxrKyYf87Jd2UZmd/Ujr/+yTdL2mypCtLe7s+Lel7qXx62ge24miKdKxX8NaerRUjJN0t6QlJH0rnelDSLqU2x6fUsd8EflgZqEKxu0BE3FWq90NJdwJfaYWP08zMzNZBDWrbR2fUWoPVnhSpSY+KiMeqX4yIv1FsrH9cRAyluFlzHPCViNidYhP/xan6UGAksCswUtLAlDHrLGBEROyZzvW1UhNzU/mvgTNK5ccCl6dHOd0qFLOiB1OkVf1N2uj/CuATAJI2B94ZEQ8Bu1Ckk23K+hFxcET8vJl6ZmZmZtZCrTVYXU6Rcaqle5/sALwQEROh2Me0dOn8toiYHxFLgEeArYF9KPZGvTclJzghlVdcnf59iGIQiqTNgO2AeyLiCWBFupxf8deUPnUWMBvYEfgr8PH0+ieAK6s7LmmjNDv8hKTywHhcrTcqaZSkSZIm3fHGrOY/GTMzM1tnNaA2fXRGrTVYbaAY3O0t6dstqC8a351hael4JcW6WgH/jIih6bFzRHy2RkylPhSzsxsAcyQ9TTGILS8FqG4/IuI54BVJu6X4K9JrMynWvhIRr6TZ4TFAeaPFN2q9mYgYExHDImLYoX0HN/KWzczMzKyWVluzGhGLgA8Bx0mqNcO6AKisS32MYm3q3gCS1pPU1M1eDwD7S9ou1e8jaftmunQscHhEDIqIQcBerDpY/bikLpK2Bd4FPJ7KrwC+AQyIiOmp7CfAd1Jq2Yo+zbRvZmZmliXa+NESaQekxyU9KelbNV7vmXZFejLd7zOo9NqZqfxxSe/Pff+1tOpuABExT9LhwF2S5la9PJZibehiYF+KmcsLJfWmWK86oonzvizpROBySZVUQ2cBT9Sqnz60rSgGuZVzzJH0uqR3p6LHgTuBzYAvpmUHUKRqvQA4pxQ7XdJXgEvTjWCvUOwC8N+NfxpmZmZmed7um6AkdQV+CbwXeBaYKOm6iHikVO2zwKsRsZ2kTwI/prjPaGeKicFdgHcCt0raPiLqSSz3plYZrEZEv9LxM8A26enfS+VXAVeVwiZSrEUtG5selZgPlY5vB/au0fag0vEk4JD0dIsadfdMhw828V5epMbnEhE3Ajc2EnNIrXIzMzOzTmY48GREzAaQdAXwEYr7iCo+AoxOx38DLpKkVH5FRCylWIb5ZDrfKvvd53IGKzMzM7MOogMkBdgCeKb0/FlWnwB8s066QX4+sFELY7O16jIAa1rPhvyMv/fMzPsaf/Tg57LbeH5S/vLbYz/WIzvmt+Pyr218Ykn+31NbDHg9O2b9dyzKjrnpqS2z6i+q40/D/bfJz6FxQB3t9BvaOzum2xHvy46ZMerO7JihM/J3g3twz7Oz6verIxn3kPN2bL5SlcH/eKD5SlV6nf7l7Ji5R7V0Y5a3bHztH7Jjvvn5k7Jjnp60flb9bUfUvHe1SQ0L8/OyPDNxveYrVem//pLmK1W56+XNsmN61pF2aOCK/KuuS5X3O3phl/xfNt0j/4ftP93z2/nhd/N+P69LJI0CRpWKxkTEmHKVGmHVX7jG6rQkNpsHq2ZmZmYdxBqP7Jo7fzEwHdNElWeBgaXnW7J69tFKnWfTDfIDgHktjM3mZQBmZmZmVjERGCxpG0k9KG6Yuq6qznUUe94DHAPcHhGRyj+ZdgvYBhgMTFjTDrVosFraCH+KpP9Ieq70fLXrwZI2lPTF0vPtJC1O9R+VNLaZraqySLpR0t1VZX+WdFTmeT4gaaKkx1JfL5fU7LUESd0kvZbbbzMzM7OytzvdalqDeipwM/AoRRKlmZK+L+nIVO0PwEbpBqqvAd9KsTMpEiw9AtwEnLKmOwFAC5cBRMQrFGlQkTQaWBgRP2siZEPgi8BvSmWPR8TQNEi9DTiaRrI+5ZC0EUVq1iWStoqIf9d5nt2B/wU+HBGPl+5q25piWrtct1sp45aZmZnZWiMi/g/4v6qys0vHS3gr42d17LnAua3ZnzVeBiDpG5JmpMd/peIfATuk2ckfleunQd5E0t1hkj4n6WpJN0iaI+lLkr4u6WFJ90laP9U7XdIjkqZK+nPplMcA11IMfEdWde/9ku5OqVGPSOeZJGmHUv/vSQPVbwHnRMTjqZ8REddGxL2leudKugs4VdK2aSPciby1fYOZmZlZ3TrAbgAdzhoNViUNB46j2ENrX+DLKVXpt0gzqRHxraqY3hT7pd5cKt6FYqC5D8XGsq9GxB7AQ8Dxqc43gKERsTvF9HTFscDl6XFsVRcHAgcDHwbGpIQC4yhSw5Iu8W8UEVNTHyY385b7R8RBEfG/wIXABRGxN/ByM3FmZmZmVoc1nVk9ELgqIhZFxAKKGc4DGqm7g6QpFNmfnkzrGipuj4g30ob8C4HrU/l0YFA6ngn8WdJxwHIASVuQMlWlzApdJZX3k/lrRDSk2dJnKBb6/pW3pq5HpuerkLRpmhWeJemrpZeuKB3vy1vLGP7UyHtG0qg0mzvp1kVPNlbNzMzMzDOrNazpYDVnU7bHI2IosB1wsKQPlF5bWjpuKD1v4K11te+nWAM7HJiU0oGNpNiEdo6kpykGrp8snat6B4iIiH8BC1NKsJG8NeCcCeyZKr2U+voHoF8pvrzhX4vS7EbEmIgYFhHDRvTZrrnqZmZmZlaypoPVu4CPSuotqR/FDUl3AwuAmjssR8TzwJnp0SJpYLplSrn6dWAToA/FZf8RETEopV0dzqpLAT6uwvYUSwJmpfJxqf2epVy3PwHOLq9nTW005gHScgKKpRBmZmZmayTUto/OaI0GqxExgWKt6ESKwduvI2J6upw/SdL06huskr8BG0rat4VNdQMukzSNYl3pj4FNgXcAk0r9mQUslbRXKnqSYkB9PTAqIpal8iuBT1FaAhARD1Nsv3CZpMcl3UsxC1y+9F92GnC6pAmsOvtqZmZmZq0ke6/TiBhd9fwnFLOS1fWq78wfWnotKG5oAri/Km7L0vHvSy/tX6M7A6sLImK3dHh89WulOs8DXWuUbJPnyQAAIABJREFUX89b62WrXzug6vmTwLtLRec11p6ZmZlZS3TWdaVtyRmszMzMzKzDarUsUta8V7vlLxZZqryYBbPz//7YYPNF2TGxOD97cb+GvtkxG/RYkh3z8utNLTWure+Apc1XqvKed76QVX/yM5tltzFx9ubZMQftX0ca5m6rXWho1tNfvSU7ZrI2zI7Z+VffzY45cMXirPpPR+/sNuZemJ9BcPHCntkx/zlytQ1LmnVjr3dkx3zz8ydlx6z3u4uzY54eclZW/a1fyk8OuGx+/u/BXr2XZ8e8/lqv7JjFdawZ7FvHVNu2A+Znxzz++vpZ9V/vkv9meq3M/39H1/wQYn7+++8oPLO6Os+smpmZmVmH5ZlVMzMzsw6ijonktV6HmVmVtDJtxF95DJI0TNIvWrGNpyVt3FrnMzMzM7O21ZFmVhenjfjLnqa0NVWFpG4RsaJdemVmZmbWTho66V6obanDzKzWIukQSTek49GSxki6BbhUUldJP5U0UdI0SV8oxdwl6RpJj0j6jaTV3qekayU9JGmmpFGl8sMlTZY0VdJtqayvpD+mth6W9JFUvoukCWkmeJqkwe3ywZiZmdlayelWV9eRZlZ7S5qSjudExEdr1NkLOCAiFqcB5vyI2FtST+DeNJCFIpPVzsC/gJuAj1EkIig7OSLmSeoNTJR0FcXg/XfAQRExR3rz1uXvALdHxMmS1gcmSLoV+CJwQUT8RVIPauzdamZmZmb160iD1VrLAKpdFxGVPWneB+wm6Zj0fAAwGFgGTIiI2QCSLgcOYPXB6mmSKgPigSl2E+CuiJgDEBHzSm0dKemM9LwXsBVFQoPvSNoSuDpl0FpFGlSPAvjU+sM5sJ8nX83MzKy2zjr72ZY60mC1Jd4oHQv4r4i4uVxB0iGsfjNd1KgzAtg3IhZJGk8xAFWN2EpbR0fE41Xlj0p6EPggcLOkz0XE7as0HDEGGAPwm4HH+yY/MzMzswwdes1qM24GviSpO4Ck7SVVdp0fLmmbtFZ1JHBPVewA4NU0UN0R2CeV3w8cLGmbdM7KMoCbgf+Sih36Je2R/n0XMDsifgFcB+yGmZmZWZ2ijR+dUWcerP4eeASYLGkG8Fvemim+H/gRMAOYA1xTFXsT0E3SNOAc4AGAiHiZ4pL91ZKmAuNS/XOA7sC01NY5qXwkMCOttd0RuLS136SZmZnZuqzDLAOIiH41ysYD49Px6KrXGoBvp8eb0uTnoogYWeN8g0pPj2ikH/8A/lFVthj4Qo265wHn1TqPmZmZWS5vXbW6zjyzamZmZmZruQ4zs9payrOxHU2vOm7x23bF8qz6U57ZNLuNZ7rn77h10qiB2TGXXvNgdszJU36fHbNi+u3NV6oy79uXZce8+Ox6WfWn9cz/c/kTvec1X6laHX+CzrtrcfOVqsx5PT8Z3PIe2SHMvfbF7Jigf1b92T3yvzYfHrlNdsyzf3g2O+aBnj2zY2BldsTTk9bPjxlyVnbMh2f8IKv+irv/mt1GnyX5389Lf3xfdkyPnvmf81/nLs2OGRWbZMdcu3TD5itV6dc9r349s11d6ojqWcdCS220UX5QB+HdAFbnmVUzMzMz67DWuplVMzMzs86qs96x35baZWZV0maSLpM0O6U4vb+0IX+7SelRn0hZqyplN0r6ZI26h0iaX0qlequkTdNrJ0q6KB0fJWnn9nsXZmZmZuuONh+spr1Jr6XIDPWuiNgL+CSwZQvjWy2FaUTMBK6mSJ+KpKOA7hFxRVWblRnnuyNiaETsBkwETqlx2qMoUruamZmZrZEGok0fnVF7zKweBiyLiN9UCiLiXxFxoaRBku6WNDk99oM3ZzXvkHQZMD2VXZtmZWemFKak8s+m2dLxkn5XmvHcRNJVkiamx/4p5PvAxyUNpdiL9ZRUf7SkMZJuoWq/1DTgXg94tap8P+BI4KdpBnbbVvzczMzMzNZ57bFmdRdgciOvvQS8NyKWSBoMXA4MS68NB4ZExJz0/OSImJcu4U+UdBXQE/gusCewALgdmJrqXwCcHxH3SNqKIgvVTilr1RnAXcD/RMSsUn/2Ag6IiMUpJeuBacP/jShSva6yp2tE3CfpOuCGiPhbHZ+NmZmZ2Zu8G8Dq2n03AEm/lDRV0kSKrFC/kzQduJJVL6dPKA1UAU5LWaUeAAYCgykGtHdGxLyIWJ7OUTECuCgNNq8D+ktaDyAirgdeA35V1b3rUgKAisoygIHAxcBP6ni/oyRNkjRp/Buzmg8wMzMzsze1x8zqTODoypOIOEXSxsAk4HTgRWB3ioHzklLcG5WDNMs5Atg3zYyOB3oBTW2O2CXVb2zDvQZW/wPmjVoVk+uAq5p4vaaIGAOMARi7xfGdc7GImZmZtQsPFFbXHjOrtwO9JH2pVNYn/TsAeCGlTv000NjNVAOAV9NAdUdgn1Q+AThY0gbppqijSzG3AKdWnqQ1qmviAOCpGuULKNazmpmZmVkra/OZ1YiIdNf9+ZK+AbxMMYP5TYq1rFdJ+jhwB43PbN4EfFHSNOBxiqUARMRzkn4IPAg8DzwCzE8xpwG/TDHdKNaofjGz+5U1q0rn/VyNOldQLGU4DTgmImoNaM3MzMya5TWrq2uXpAAR8QLFdlW17FY6PjPVH08pZWpELAWOaCT+sogYk2ZWr6GYUSUi5gIjm+jToKrno6uej6eY0a0VOxYYm47vxVtXmZmZmbWJtSGD1WhJIyjWsN5CsaermZmZWafT0NTdOOuoTj9YjYgz3u4+tFT3OlZNP9aje1b9Y3d+JruNLR9dPzuG9TfKDtmp+4bZMSuu/03zlaqsfOzJ7Jj1ds7PPbF88aKs+hu8kr+0uXv3ldkxPXbdIjtm413yLzwN+tPr2TGTVuR/r21y7FbZMf85/7Ws+hvUcd0tli/PjuneI//ruce8/HZm9Mz7vQGw7Yim7i+tbeuX8j5ngBV3/zWrfrcDP5HdRl1+fF92SMPK/FHFkG75vwe75n/bsNPS/G/q+V3zbmOZX8ddL/1W5vfr+e75DWmnvbJjrOPq9INVMzMzs7VFZ80y1ZY8WDUzMzPrIDxUXV27JgWQtJmkyyTNTqlT75f00fbsQ1V/jkgb9j8q6TFJP3u7+mJmZmZmq2u3waokUdz8dFdEvCsi9qLYIWDLFsbnLyps+nxDgIuA4yNiJ2AIMDsj3rPSZmZm1qoa2vjRGbXnzOphwLKIePOOmYj4V0RcKGmQpLslTU6P/aDIXCXpDkmXAdNT2bVpVnampFGVc0n6rKQnJI2X9DtJF6XyTSRdJWlieuyfQr4BnBsRj6W+rIiIX6WYD0t6UNLDkm6VtFkqHy1pjKRbgEsl7SJpgqQpkqZJGtzmn6KZmZnZOqQ9Zwd3oUgCUMtLwHsjYkka8F0ODEuvDQeGRMSc9PzkiJgnqTcwUdJVQE/gu8CeFBmlbgempvoXAOdHxD2StgJuBiozqT9vpD/3APukhAafoxjY/r/02l7AARGxWNKFwAUR8RdJPWg8A5eZmZlZs3yD1eretkvZkn5JkcJ0GTACuCilRF0JbF+qOqE0UAU4rbTOdSAwGHgHcGdEzEvnvrJ0jhHAzsUqBAD6S2puD6EtgXGSNgd6AOX2r4uIxen4fuA7krYEro6IWTXe5yhgFMDJA4ZzWB9PvpqZmZm1VHsuA5hJMfMJQEScArwH2AQ4HXgR2J1iRrVHKe7Nzf8kHUIx+Nw3InYHHqZIBtDUZnddUv2h6bFFRCxI/WlsI7YLgYsiYlfgC6mN1foTEZcBRwKLgZslHVZ9oogYExHDImKYB6pmZmbWlGjjR2fUnoPV24Fekr5UKuuT/h0AvBARDcCnafxy+gDg1YhYJGlHYJ9UPgE4WNIG6cano0sxtwCnVp6k2VuAnwLflrR9Ku8i6Wuldp5Lxyc09oYkvQuYHRG/AK5j1dSxZmZmZraG2m2wGhEBHEUxqJwjaQJwCfBN4FfACZIeoLh831gqlZuAbpKmAecAD6RzPwf8EHgQuBV4BJifYk4DhqUboB4BvphipgFfBS6X9CgwA9g8xYwGrpR0NzC3ibc1EpghaQqwI3Bpyz8RMzMzs1V5N4DVteua1Yh4gWK7qlrKs5JnpvrjgfGl+KXAEY3EXxYRY9LM6jUUM6pExFyKQWWt/twA3FCj/O/A32uUj656fh5wXiP9MTMzM7M1tDbtFTpa0giK9aW3UOzpamZmZtZpeDeA1a01g9WIOOPt7kNbeKVL3jdt135N3WvWSEyXOi4MvP5qdkjfOr7dYm5TqzBqa5i3MDumHr3XX55Vv2f+W2HZ0vzPTD17NF+pWpf875vXFy/NjunVPb+dWLosO6Zvr8yY5X2ar1Ol4cV52THzX+udHbOsjtVaK7MjoGHhiuyYZfPz+9ZnyeLmK3USXbvn/+7s076JI7Nk/u+G7nW00aD83wF1DVSWLqknyjqotWawamZmZtbZeV51dR33TzwzMzMzW+e122BV0maSLpM0O6VLvb+0uf/bQtLfJd3/dvbBzMzMrMK7AayuXQarKtJHXQvcFRHvioi9KHYF2LKF8a2exlTS+hRJCtaXtE0jdbxMwszMzOxt1F4zq4cByyLiN5WCiPhXRFwoaZCkuyVNTo/9oMhWJekOSZcB01PZtWlWdmZKY0oq/6ykJySNl/Q7SRel8k0kXSVpYnrsX+rT0cD1wBWUttOSNFbS/0i6A/ixpL6S/pjiH5b0kVSvZr/NzMzM6hVt/F9n1F4zh7sAkxt57SXgvRGxRNJg4HKKlKsAw4EhETEnPT85IuZJ6g1MlHQV0BP4LsUs6QKKTFlTU/0LgPMj4h5JWwE3Azul144FvkeR5vVvrLpf6vbAiIhYKemHwO0RcXKajZ0g6dZm+m1mZmZmreBtucwt6ZfAAcAyYARwUUqDupJioFgxoTRQBTittM51IDAYeAdwZ0TMS+e+snSOEcDOemurjP6S1qNI87odcE9EhKQVkoZExIxU78qIqOz+8j7gSEmVrbF6AVsBzzfR7/J7HQWMAjh5wHAO6zO4ZR+SmZmZrXM667rSttReg9WZFJfdAYiIUyRtDEwCTqeY3dydYllCeXO0N9OuSjqEYvC5b0QskjSeYuDY1KZtXVL9VTb2k3QSsAEwJw1k+1MsBTirut10/qMj4vGqc4xuot9viogxwBiAv7zz+M45/25mZmb2NmmvNau3A70kfalUVtmFewDwQkQ0AJ8GGruZagDwahqo7gjsk8onAAdL2iDdEHV0KeYW4NTKkzQLCsUSgMMjYlBEDAIqN3zVcjPwX+kmMSTtkdlvMzMzsxZpINr00Rm1y2A1IgI4imJQOUfSBOAS4JvAr4ATJD1AcSn9jUZOcxPQTdI04BzggXTu54AfAg8CtwKPAPNTzGnAMEnTJD0CfFHSIIrL+A+U+jcHeF3Su2u0ew5Foo5pkmak52T028zMzKxFoo0fnVG7rVmNiBdofPZyt9Lxman+eGB8KX4pcEQj8ZdFxJg0s3oNxYwqETEXGFmj/hY1+rdnOnywqnwx8IUa9WfV6reZmZmZtZ61ZR/R0ZJGUKxhvYViT1czMzOzTqWzXqpvS2vFYDUizmi+1ttvfh2rWgeubOr+sdVNvOcd2W1s3id/BcPL//tA85WqDF2+eXbMS395JjtmxbL81S0LFvbOjlmvX8176hq1xfIV2W3c/8aG2TG9x+V/ZuqS/8txwcqNs2P61/Ez8Or1z2fHdFHe13PjFfnv/9kbVzZfqcrrS/tmxyxX3u8AgMXKfz/PTFwvO6ZX7+XZMUt/fF9eQG79Om1x62+zY/5vyFnNV6rS0Cv/a/NSt/zfaa93yf++2W1J3u+opV3yhxD5PzWwQR0/n0su+mN2TK8DP50dY+1jrRismpmZma0NvHXV6tprNwAzMzMzs2ytOliVdL6kr5ae3yzp96XnP5f0tTVsY6ykY9LxeEmPp7v9H5N0UcoyVc95R5c2/i+X7yPpQUlTJD2a9ldF0omSXk7lUyRduibvy8zMzMzpVlfX2jOr9wH7AUjqAmxMkWq1Yj/g3lZu87iI2I3izvylwN9b+fyXAKMiYigwBPhr6bVxETE0PT7Tyu2amZmZrfNae7B6L2mwSjFInQEsSBv29wR2AqZI+qmkGZKmSxoJoEJj5RdJekTSjcCmtRqOiGXAN4CtJO2eYo+XNCHNfP5WUtdUfrikyZKmSrqt+lySPi/pH5J6p/ZeSG2sjIhHWu3TMjMzMytpaONHZ9SqN1hFxPOSVkjaimLQej/Fnqb7UmzUPw34EDCUIk3pxsBESXel+rXK9wV2AHYFNqPY9L/mbX4RsVLSVGBHScso9ljdPyKWS/oVcJykfwC/Aw6KiDmSVrndWtKpwPuAoyJiqaTzgcdTetebgEsionIb+EhJB6TjCyLi4vo/PTMzMzOr1ha7AVRmV/cD/odisLofxWD1PuAA4PKIWAm8KOlOYO8myg8qlT8v6fZm2q/s1/EeijSqE1Om1N7ASxRpWu9KWauIiHml2E8Dz1IMVJen178v6S8UA9hPUaRqPSTVHxcRp9IESaOAUQDHrj+cA/oNbqb7ZmZmtq7qrOtK21Jb7AZQWbe6K8UygAcoZkcr61Ub2/ytqU3hWvSVS5f5dwUeTee7pLSmdIeIGJ3KGzvfDGAQsOUqjUc8FRG/phgA7y5po5b0J8WOiYhhETHMA1UzMzOzPG0xWL2X4lL/vLTGcx6wPsWA9X7gLorL510lbUIxczqhmfJPpvLNgUNrNSqpO3Ae8ExETANuA46RtGl6fUNJW6c+HCxpm0p56TQPU6RWvU7SO9PrH5Te3JV7MMWexq+t+cdkZmZmtiqvWV1dWywDmE6x5vSyqrJ+ETFX0jUUA9epFDOc34iI/zRTflg6xxPAnVXt/UXSUqAncCvwEYCIeETSWcAtaWeC5cApEfFAujR/dSp/CXhv5WQRcU/awupGSe+lWBpwvqRFwAqK3QdWqo6sMmZmZmaWp9UHq2ltaf+qshNLxwF8PT1oYXnNdaERcUgzfRkHjKtR/g/gH1Vlo0vHNwM3p6efbOTcY4GxTbVvZmZmlqMhvGa1mtOttqM+dcy/537LPtetG9uvzMtZ/+9F/TJbgX12fj07ZvGL2SEsWdQ9O6ZXn/x85QuX5bfz4it5+ee7AC91y/uR2zkWZdUHaFiZP+u/ZEGP7JitN5yfHTN3fouXe7+p96b52cQf/lf/5iuVzO2e/5kN2GRxdkzXbvm/BFbMy3svAD3J/3r2Xz/v9wbA66/1yo7p0TPv61nP93PX7vmf8/8NOSs75gMzfpAdM3Wv72bH9K7j/x3bLVuRHTO/S9es+vVcUh7QkP/zPLNn/lAlGjzgW5t4sLqWyR2oWvvJHaiamdm6x8Ps1bXFDVZmZmZmZq3CUz1mZmZmHUSD51ZX06ozq5K2lPR3SbMkPSXpAkn5i6eabmO0pOdSCtUZko5spfMubKR8B0njU3uPShqTyg+RND+VT5F0a2v0w8zMzMze0mqD1bQX6dXAtRExGNge6Aec21ptlJwfEUOBjwN/TFtQtaSP9cwk/6LSXkTsBFxYeu3uUtKBEXWc28zMzOxN0cb/dUatObN6GLAkIi6GN7ewOh04WdKX04zrTZIel/TflSBJx0uakGYnf5uyUCFpoaRzJU2V9ICkzaobjIhHKfY+3VjS1pJukzQt/btVOs9YSf8j6Q7gx5L6SbpY0vRU9+hSX2q1tzlFCtZKm9Nb8TMzMzMze5OTAqyuNQeruwAPlQsi4nXg3xRrY4cDxwFDgY9LGiZpJ2AksH+aKV2Z6gD0BR6IiN0pslh9vrpBSe+m+OxfBi4CLo2I3YC/UMyIVmwPjIiI/wd8F5gfEbumurc30975wO2S/iHpdEnrl857YGkZwHdqfSiSRkmaJGnS+DdmNfHxmZmZmVm11rzBStTecaFS/s+IeAVA0tXAARSzonsBE1NGqN4UGaUAlgE3pOOHKGWZAk6XdDywABgZESFpX+Bj6fU/AT8p1b8yzfQCjKC00X9EvNpUexFxsaSbgcMpsmN9QdLuqd7dEfGhpj6UiBgDjAEYu8XxnXP+3czMzNqFb7BaXWsOVmcCR5cLJPUHBlLMmFZ/+kExkL0kIs6scb7lKXsVKb7c1/Mj4mfN9Kfc3hvlbtXoS5PtRcTzwB8p1sfOAIY007aZmZmZtYLWXAZwG9BH0mcA0trTn1OkJF0EvFfShpJ6A0cB96aYYyRtmmI2lLR1ne3fx1szpscB9zRS7xZK6VslbdDUSSUdLql7On4HsBHwXJ19NDMzM2uUb7BaXasNVtOs5Ecp1qPOAp4AlgDfTlXuobg8PwW4KiImRcQjwFnALZKmAf+kuKGpHqcBJ6XzfBr4SiP1fgBskLa9mgoc2sx53wdU6t4MfD0i/lNnH83MzMw6pTSp+M+0Rek/a034SRoq6X5JM9ON7CNLr42VNKd0v8/QlrTbqkkBIuIZ4MPV5Wk96ksRcWqNmHHAuBrl/UrHfwP+lo5HN9L20xQ7ElSXn1j1fCFwQkZ7XwO+VqP+eGB8rb6YmZmZ1aOD37H/LeC2iPiRpG+l59+sqrMI+ExEzJL0TuAhSTdHxGvp9a+ncVaLOYNVO6pnGnu58uo/3aVXdhuL6ujY/8zcIjvmwGUrsmOOWvpydsx+y/JXkryzV37uii7kfXF2W5p/+eXRLn2yY25+rXd2TJ/I/EYDdn49/+vZv45fwzc9NDA7Zm7ml7P/yubrVPvl8/kXgXrW8TkPUf73zbuW5cfc9fJquwM2a3H+2+Gvc5dm1R/SbcPsNvrU8du2oVf+ZzZ1r+9mx5z50DnZMb/d4+zsmIm9umbH7LEk7wdhhfLbmN0jf9ixfh2jt9ET35Edc0F+M+uijwCHpONLKCbtVhmsRsQTpePnJb0EbAK8Rp1aNYNVYyJibK1ZVTMzMzN7S0S06WMNbRYRL6R+vgBs2lRlScOBHsBTpeJz0/KA8yX1bEmj7TJYNTMzM7O3X3n/9/QYVfX6rem+nurHRzLb2ZziXqWTIqIyP34msCOwN7Ahqy8hqCl7Pl7SSqCcxemKiPhRHed5GhgWEXNzY1tw7kHADRExRNIhwN+B2UAviv5+rxXaGA+cERGT1vRcZmZmZtD2+6yW939v5PVG08dLelHS5hHxQhqMvtRIvf7AjcBZEfFA6dwvpMOlki4GzmhJn+tZs7o4ZZvqTO6OiA9J6gtMkXRDRDzUXJCkbhGRvzDPzMzMbO1zHcVN6j9K//69uoKkHsA1FFlFr6x6rTLQFcU2pjNa0mirLQOQ9LSk70maLGm6pB1TeT9JF6eyaZKOrhH7tdI081dTWV9JN0qamspHpvK9JN0p6SFJN6eRfaV8qqT7gVNq9TEi3qDITrWtpF6lfj0s6dB0nhMlXSnpeoo9WZH0jVRvqqTyLPLHJU2Q9ISkA1vrszQzM7N1U0MbP9bQjyj2zZ9FkenzRwCShkn6farzCeAg4MQaW1T9RdJ0iiv0G1NsJ9qsemZWe0uaUnp+Xtp+CmBuROwp6csUU7ufA74LzI+IXdMbWmVPLkl7AScB76bILvWgpDuBdwHPR8QHU70BaXP+C4GPRMTLaQB7LnAycDHwXxFxp6Sf1uq4pI2AfYBzSAPaiNg1DaxvkbR9qrovsFtEzJN0BMXo/90RsUhS+dbUbhExXNIHgP+mSOVqZmZmttaJiFeA99Qon0Qx5iMi/gz8uZH41bYYbYnWXgZwdfr3IeBj6XgEb2WWIiJerYo5ALgmzXoi6WrgQOAm4GeSfkyx/vRuSUMoUp3+M+3d2hV4QdIAYP2IuDOd80/AEaU2DpT0MMUfFT+KiJmSfkAx8CUiHpP0L6AyWP1nRMwr9f/iiFiU6s4rnbf8fgfV+kDSwuVRACcOGM6hfQfXqmZmZmbWabNMtaXW3me1soHeytK5BU1+8jV36ouIJ9Ks6weA8yTdQrEGYmZE7LvKCaT1m2nj7oj4UEvaTd6oqtfYuWu931WUFzJfusXx/g40MzMzy9AeW1fdAry5x2r1MgDgLuAoSX3SDVAfBe5OWQ8WpenknwF7Ao8Dm0jaN52ru6RdUlaE+ZIOSOc8rgX9uqtSL13+3yqdv1b/T5bUJ9XN36HazMzMrAUaiDZ9dEb1DFZ7lxbMTqm64aiWHwAbpJukpgKHll+MiMnAWGAC8CDw+4h4GNgVmJDWx34H+EFELAOOAX6czjUF2C+d6iTgl+kGq8UteB+/Arqmhb7jgBMjYrXUKhFxE8Xdb5NSX1q0zYKZmZmZrbnsZQARUTO/WkQMKh1PIqXjiv/P3nnH21FV/fv5pkASQu/SOypVAoKCdBSlqNQACqIg7+uLvCJFAWkqqPCCNIGggKBUqaJAAAkBqQHSaMIP6UVKCIRA6vr9sfbhzj13Zs7MSS65N6znfs7nnjNnr9l75kxZs/YqZpPw9AZl7U8DTmv6/lbg1hy50XiUWfPyh4F1M4uOT8tH4OXAmtt/COyXs/xiXHnOLvsVKeIts2yLzPs3KfBZDYIgCIIgqMpsqDI11xEVrIIgCIIgCIIei0KD//g4Y/n6AVZ9akqsMbV+DYO3+uYay0vp18Zh005+t3aepvq3cUx/0Kcs3i6fuvvgjX71+1h4Rv1tmXdmGzJt7LMX56l/3MzbxkEwqI3tqft7Dm6jj3Z4r43jbMrHZFJo57fp38Zuq3t+1j/K2uM/bZyfA9vYZ++38Xt+/9ETa8tcsP6xtWXqbk/9PQbztXGuTW+jo8Ez6/84O752eTubNNv58nLbd+sF6dYXb+4R21mH2Z0NIAiCIAiCIGiTSF3VlXADCIIgCIIgCHosPUJZTaVPH0zlTB+TdEJavkMqhTpG0uOSvt/m+p/LlEsdLmmp2TDm/SSdPavrCYIgCIIgaBCpq7rSU9wApgBbmdmkVFL1Hkm348n0NzKzlyTNy6xF3G9pZm9KOgkaPQl7AAAgAElEQVQ4CvhhFSFJfc1sxiz0GwRBEARBELRJj7CsmjMpfeyfXlNxZfqt1GaKmT0FIGm3Rt5WSSPTsv0kXSvpFklPS/pNQXcjgVWTzNBkcR2fyrqSlk+SdKKkB4BNJG0o6d7U34OS5k9NP1WhvyAIgiAIgkqYWbe+eiM9xbKKpL7Aw7gieY6ZPSDpRuB5SXcANwGXm9lM4Fjgy2b2ciq12mA9YH3cUvuUpLPM7MWmrnYAxqUKWb8GNgAmAMMlfd3MrgfmA8ab2bGS5gGeBPYws4ckLUBH0YEq/QVBEARBEARt0iMsqwBmNsPM1gOWBTaStJaZfQ/YGq9udRhwYWr+T+BiSQfQObPJHWY2MSX8fxxYIfPdnakC1QLAycCGwAgze8PMpgN/pqPYwAzgmvR+DeBVM3sojfPd1L5VfwBIOlDSKEmj7p30dLu7JwiCIAiCTwDhs9qVHqOsNjCzd/CKU19Jn8eZ2enAtsAuadlBwDHAcsBoSYsm8Wy51Bl0thxvaWbrmdm3Ux9lecY+zPipCgp/3bL+GtszzMyGmNmQLwxeraTLIAiCIAiCoJkeoaxKWrwxnS9pILAN8KSkLTLN1gOeT21WMbMHzOxY4E1caa3LA8DmkhZLLghDgbty2j2J+6ZumPqeX1KPcZ8IgiAIgmDuwbr5rzfSU5SupYE/JqWxD3AVrjheKel83Ef0fWC/1P4USavhVs87gDG4MlsZM3tV0k+BO9N6/m5mN+S0myppD+CspEh/gCvTQRAEQRAEQTfTI5RVMxuLByo189WC9t/MWXxxejXa7JB5v2LBei4DLstZPrjp80PAxlX7C4IgCIIgaIeZvTRivzvpEW4AQRAEQRAEQZBHj7CsflJYsI3SAh/WfJyYrPrPHwNn1n+Km8dm1paZobKYtnyemadv60ZN9Lf6/aw8dXrrRk3U3dOLTK2/n5/r37+2zJIzptWW6dOGH9MS0+rv52ltHAPL2pTWjZp4aea8tdq3Y8eY1Kf+tiw9vf5x9mEb5/S/56kvs9z0+heoVRacWFvm+imL1Gr/6Sn1rzXt8G4bv+eqbVw3HhpQ/5p2wfrH1pY54NETa8sM/+zRtdpP7lP/OJtefzczozQeOp/F+9S/bvQUwq7albCsBkEQBEEQBD2WsKwGQRAEQRD0EHprLtTupFstq5KOlvSYpLGSRkv6fHf2VzCG4yW9nPofL2mn2bTeSa1bBUEQBEEQBLNCt1lWJW2Clzb9nJlNkbQYME8FuX6ZClGzi9PN7FRJnwbulrREKts6J8YSBEEQBEGQS1hWu9KdltWlgTfNPDrCzN40s1ckbSjpXkljJD2YkuzvJ+lqSX8FhgNIOlzSQ8kqe0JjpZL2SXKjJZ2fcrMiaZKkX6b13i9pyeYBmdkTwHRgMUkrSLojrf8OScun9Vws6TRJdwK/ljRY0kWSxqW2u2TGUtpfEARBEARBMGt0p7I6HFhO0r8k/U7S5pLmAa4EDjGzdfHk+h+k9psA+5rZVpK2A1YDNsKT/W8g6UvJMroH8EUzWw8vcbp3kp8PuD+tdyRwQPOAkhvCTOAN4GzgEjNbB/gzcGam6erANmb2Y+BnwEQzWzu1/UfV/oIgCIIgCOpgZt366o10m7JqZpOADYADceXwSuD7wKspyT5m9m5mmv02M3s7vd8uvR4FHgHWxJXXrdM6H5I0On1eOclMBW5K7x8GVswM50ep/anAHua/1iZ0FAS4FNg00/5qM2vkcdkGOCezXRMq9PcRkg6UNErSqBHvP53XJAiCIAiCICigW7MBJIVvBDBC0jjgBxSnEHs/817AyWZ2fraBpIOBP5rZT3Pkp1nHI8MMOm/b6WZ2aqvhlowlb8xl/XWs1GwYMAzg4mX26Z2PNEEQBEEQfCyEz2pXus2yKmkNSatlFq0HPAF8StKGqc38kvKUvFuB/SUNTu2WkbQEcAewa3qPpEUkrdDmEO8F9kzv9wbuKWg3HPifzHYt3GZ/QRAEQRAEQU2607I6GDhL0kJ4UNMzuEvARWn5QNxfdZtmQTMbnvxT75NXvJkE7GNmj0s6BhguqQ8wDbfWPt/G+H4IXCjpcNxN4TsF7X4BnCNpPG5BPQG4to3+giAIgiAISrGwrHah25RVM3sY+ELOV28CGzctuzi9svJnAGfkrPdK3P+1efngzPu/AH9J748vGN9zwFY5y/dr+jwJ2Ldqf0EQBEEQBO3SW4OgupMotxoEQRAEQRD0WKLc6sfIAjNa1iHoQj+r9zwxqHWtgy6826dvbZkP2pCZgWrLDK6/OW09gb3cv/6pMKjm2AbPrL8xU+rvMl7q17L2RhcGzqz/JN/G0Fhw5ozWjZp4h/61ZfrWHFz9UcGANqwfr/Wrf5wtPr3+6BZo47yZovq/6FPvLlRbZnDNn3Ni3/pndJ82DFPrfFi//svENq6D639Y//d8vY3jZvhnj64ts91jv6zV/oa1f1a7j3f71D/O3mvjoj5o6oD6Qj2ECLDqSlhWgyAIgiAIgh5LWFaDIAiCIAh6COGz2pVeYVmVNCOVVx2fyrIOanM9k5o+/0jSh5IWnD0jDYIgCIIgCGYnvUJZBT4ws/XMbC28ctRBs2m9Q4GHgG/kfVmQAzYIgiAIgqBbmIl166s30luU1Sx3A6sCSDo0WVvHS/rfRoOi5VkkrYLngj0GV1oby/dL1tu/4gUBkHS4pIckjZV0Qqbt9ZIelvSYpAO7ZWuDIAiCIAg+wfQqy2GydG4P3CJpAzyR/+fxwOQHJN2FK+BdlpvZo02rGwpcjiu/a0hawsz+k77bBFjHzN6WtB2wGrBRWt+Nkr5kZiOB/VObgcBDkq4xs7e6cRcEQRAEQTAXE0UButJbLKsDJY0GRgEvAH8ANgWuM7P3U+L+a4HNSpY3sydwhZnNTG12y3x3m5m9nd5vl16PAo8Aa+LKK8APJY0B7geWyyz/CEkHSholadTwyc+0vweCIAiCIAg+gfQWy+oHZrZedoFUmBSwZRI3SevgiuVtaTXzAM8C56Qm7zet72QzO79pHVvgpWI3MbPJkkYAXRK7mdkwYBjAtUvtFY9LQRAEQRAUMjOyAXSht1hW8xgJfF3SIEnz4UFSd5cszzIUON7MVkyvTwHLSFohp59bgf0lDQaQtIykJYAFgQlJUV2TriVkgyAIgiAIglmkt1hWu2Bmj0i6GHgwLfp9wy+1aHmGPXHf1yzXpeWvN/UzXNKngfuSFXYSsA9wC3CQpLHAU7grQBAEQRAEQduEz2pXeoWyamaDC5afBpxWY/ng9H+lnO8OzXy8uOm7M4AzcobQrPAGQRAEQRAEs5FeoawGQRAEQRB8Egif1a6EsvoxMrFv97sIv9av/k86eMbM2jIT+tXflhWnTq8t82zf+tszsI3zfFrLsLyuzKgp087v384RM6WNbfmgb32hwfUPG95r4/hsY3Nq74N52jhmprcxsP5t9DOhb9/aMktNm1FbZlKf+kfbu33q74S6vUxs4yToX1+EKX3qH5ttnAJMV/3fs51zYHIbv+cNa/+sVvudx/28dh9/WvfY2jJL1D+ceaONe1TQcwllNQiCIAiCoIcQPqtdiUePIAiCIAiCoMfSq5VVSSbp0sznfpLekHRT+ryTpJ/UXOfxkk5uWraepCdayI2QNKROX0EQBEEQBFlmmnXrqzfSq5VVPHn/WqncKcC2wMuNL83sRjP7Vc11Xg7s0bRsT+CytkcZBEEQBEEQtEVvV1YBbga+lt4PxZVNACTtJ+ns9H43SeMljZE0Mi3rK+lUSeMkjZV0sJk9Bbwj6fOZPnYHrkgy56byqY9JOuHj2MAgCIIgCD4ZWDf/9UbmhgCrK4Bj09T/OsCFwGY57Y4FvmxmL0taKC07EFgJWN/MpktaJC2/HLemPiBpY+AtM3s6fXe0mb0tqS9wh6R1zGxsN21bEARBEASfIHrrVH130ustq0lRXBG3qv69pOk/gYslHQA0codsA5xnZtPTut5Oy68AdpXUB1daL8+sZ3dJjwCPAp8FPlM2PkkHJkvsqBHvP13WNAiCIAiCIGii1yuriRuBU+msVHbCzA4CjgGWA0ZLWhRPX9flEcbMXgSeAzYHdgGuApC0EnAYsLWZrQP8DRhQNjAzG2ZmQ8xsyBbzrVZ/y4IgCIIg+MQQbgBdmVuU1QuBE81sXFEDSauY2QNmdizwJq60DgcOktQvtVkkI3I5cDrw/8zspbRsATyoa6KkJYlyq0EQBEEQBN3K3OCzSlImz2jR7BRJq+HW1DuAMcB4YHVgrKRpwAXA2an91WmdB2f6GSPpUeAx4FnctSAIgiAIgmC2YNZObbS5m16trJrZ4JxlI4AR6f3FwMXp/TdzVjEdODS9mtfzBjlV+8xsv4KxbFFt1EEQBEEQBEFVerWyGgRBEARBMDcxs5f6lXYnoax+jAyaWf8AfLev6vUxo34fk/rWd11eder02jIv96t/uC08o7YIfdo40fur3n4GqDtRM61+Fyw2vf62tHOZ699GqpR2Jqom9at/rC03rf6x9sI89Y61+ds4b+ZtY6ruzb59WzdqYvDM+v1M6lN/P7dzDAxoY7/1qRkqMXhG/e2f2cb53MalhgVn1pd6tuaxCbB4G9eB6W1cb97tU0/oT+seW7uPfcacWFvmiCFH1ZbZ5oM2dkDQYwllNQiCIAiCoIdgkWe1C3NLNoAgCIIgCIJgLmSuUFYlmaRLM5/7SXojVbUqk1tS0k2pBOvjksqKCiBpRUnjC74bIWlIe1sQBEEQBEHgPqvd+eqNzC1uAO8Da0kaaGYfANsCL1eQOxG4zczOAJC0TjeOMQiCIAiCIKjJXGFZTdwMfC29H0qmmpWkRSRdL2mspPszSunSQCPhf6N0K3JOkTRe0jhJezR3JmmgpCvSOq8EBnbXhgVBEARB8MnAzLr11RuZm5TVK4A9JQ0A1gEeyHx3AvBoKpF6FHBJWn4O8AdJd0o6WtKn0vJvAusB6wLb4AUFlm7q77+AyWmdvwQ26I6NCoIgCIIg+CQz1yirySq6Im5VbfY93RS4NLX7B7CopAXN7FZgZbxy1ZrAo5IWT+0vN7MZZvY6cBewYdM6vwT8KdP32LxxSTpQ0ihJo26f/Mysb2gQBEEQBHMtM8269dUbmWuU1cSNwKlkXAASeQnXDMDM3jazy8zsW8BDuBJaNUFby1/dzIaZ2RAzG7LNoFUrrjYIgiAIgiCAuU9ZvRA40czGNS0fCewNIGkL4E0ze1fSVpIGpeXzA6sAL6T2e0jqmyytXwIeLFnnWrjrQRAEQRAEQdtYN//1RuaWbAAAmNlLwBk5Xx0PXCRpLDAZ2Dct3wA4W9J0XHH/vZk9JGkUsAkwBreeHmFmr0laMbPOczPrHE1XZTYIgiAIgiCYReYKZdXMBucsGwGMSO/fBnbOaXMKcErOcgMOT6/s8ueAtdL7D4A9Z3XsQRAEQRAEDXprxH53Mre5AQRBEARBEARzEXOFZbW38EGfqnFbHcys2X6a6vcxrb4Iz85T/9BZalrdrYG3+tV/nlpwRm0RPmxjHwyo+fQ7X/3N59V+9Qc2qK2H8o/nuGljF/BcG8failOn12r/9Lz1+xg0s29tmXasAy/1ry+15PT6B8FrbfTTt41jbd6aMq+0Ma52bmwLt7HPHmvjuFmojZNgehvn2ow2zun3au7qJdq41h4x5KjaMr8ZdVJtmfPXP7a2TE+ht1aZ6k5CWQ2CIAiCIOghhBtAV8INIAiCIAiCIOixzHXKaiqVeo+k7TPLdpd0S07b/VM51bGptGqXIKym9hdL2jVn+RaSbpo9WxAEQRAEwSeVKArQlbnODcDMTNJBwNWS7gT64uVQv9JoI0nAcsDRwOfMbKKkwcDic2LMQRAEQRAEQT5znbIKYGbjJf0VOBKYD7gEmCHpCeBOPIfq/wLvAZOSzKTGe0nrAecBg4D/B+xvZhOyfUj6CvBb4E3gkY9hs4IgCIIgmMsJn9WuzHVuABlOAPYCtgd+k5atAVxiZusD9wCvA/+WdJGkHTOylwBHmtk6wDjguOyKJQ0ALgB2BDYDlurODQmCIAiCIPikMtcqq2b2PnAlcKmZTUmLnzez+9P3M3DXgF2BfwGnSzpe0oLAQmZ2V5L5I15uNcuawL/N7OlUQOBPReOQdKCkUZJGjXj/6dm2fUEQBEEQzH3MxLr11RuZa5XVxEw6p3Z8P/ulOQ+a2cl4Napdaqy70i9uZsPMbIiZDdlivtVqrD4IgiAIgiCY25XVQiR9StLnMovWwy2vE4EJkjZLy78F3NUk/iSwkqRV0ueh3TvaIAiCIAg+CZhZt756I3NlgFVF+gOnSvoU8CHwBnBQ+m5f4DxJg4Bnge9kBc3sQ0kHAn+T9Cbu/7rWxzbyIAiCIAiCTwhztbJqZsdn3j9HRqE0s+eBrQrkRgMb5yzfL/P+Ftx3NQiCIAiCYLbQW3OhdidztbLa05jSRn3nCTXLj0/oC/+9zeu1ZN5/ql4ddYDFrv9DbZl2ajV/98w2DNZTp7Ru08TMsePry0yYVFvmuhsWrdV+x0Xr/ZYAS56+R20ZPpxcW2Ty766pLXP56OVqyxxwy3daN2riP/vVqyX+8qtL1+5jn7PqH5sfXHxzbZm3nx5QW+acyQvXljnpZ8vWlrGJE2vLaNF654A+vUHtPpjyYW2RD8++sLaMzayvVBz/UP3kMdt8WP/msXif+tfBQVPrHWtv9KvvSbjNB/W3pZ17x/cfPbG2TNAaSYvgwesrAs8Buzen9kztZuDZlABeMLOd0vKVgCuARfC0n98ys6mt+v3E+qzOrdRVVIOPj7qKahAEQfDJw7r5bxb5CXCHma0G3JE+5/GBma2XXjtllv8aOD3JTwC+W6XTUFaDIAiCIAiCKuyMp/Qk/f96VcFUPXQr4C915cMNIAiCIAiCoIfQw31WlzSzVwHM7FVJSxS0GyBpFDAd+JWZXQ8sCrxjZg3fw5eAZap02uOVVUlH45WoZuA5U79vZg8UtL0YuMnM/pL3fabN5sDEtL4fmNl9Oe0OAiab2SWzug1BEARBEAQ9gZTN6MDMomFmNizz/e3kV+Y8ukY3y5vZK5JWBv4haRzwbk67Spp5j1ZWJW0C7AB8zsymSFoMmGc2rPpwM/uLpO2A84F1mvrtZ2bnzYZ+giAIgiAIKtPduVCTYjqs5Pttir6T9LqkpZNVdWngPwXreCX9f1bSCGB94BpgoaRjTQeWBV6pMuae7rO6NPBmo1yqmb2ZNPVjJT0kabykYckPohOSNpB0l6SHJd2admozI4FVU/sRkk6SdBdwSCq9elj6blVJt0saI+mRRjEASYencYyVdEJ37YQgCIIgCIIewI14LnrS/xuaG0haWNK86f1iwBeBx1N5+jvxMveF8nn0dGV1OLCcpH9J+p2kzdPys81sQzNbCxiIW18/QlJ/4CxgVzPbALgQ+GXO+nekI7UCwEJmtrmZ/V9Tuz8D55jZusAXgFeTVXY1YCO8+tUGkr7U3IGkAyWNkjTq7vefrrn5QRAEQRB8kujh2QB+BWwr6Wlg2/QZSUMk/T61+TQwStIYXDn9lZk9nr47EjhU0jO4D2ulPJg92g3AzCZJ2gDYDNgSuFLST4D3JB0BDMJzdT0G/DUjugZeAOC2ZHTtC7ya+f4UScfgVauyaROubB6DpPmBZczsujSmD9Py7YDtgEdT08G48jqyaRs+Mrefv+w+PdprOgiCIAiCoAgzewvYOmf5KOB76f29wNoF8s/iRr5a9GhlFcDMZgAjgBHJQff7uI/pEDN7UdLxQHMmYwGPmdkmBas9vCAI6/2cZUUZjAWcbGbnt9iEIAiCIAiCSnS3z2pvpEe7AUhaQ9JqmUXrAU+l929KGkyH70OWp4DFU4AWkvpL+mw7YzCzd4GXJH09rWteSYOAW4H90xiQtExJCocgCIIgCIKWmFm3vnojPd2yOhg4S9JCeK6uZ/B0C+/gvqbPAQ81C5nZVEm7AmdKWhDfzt/i7gLt8C3gfEknAtOA3cxsuKRPA/clV4NJwD4URMYFQRAEQRAE9enRyqqZPYwHNDVzTHo1t98v83400CXgKdumafkWTZ+Pz7x/Gq+60CxzBnBG/uiDIAiCIAjq0Tttn91Lj3YDCIIgCIIgCD7hdLdvRLwq+5Ac2J3tQ+bjk+mp4wqZnjuukOm54wqZnjuuj1MmXnP2FZbVnsOBrZvMUvuQ+fhkeuq4Qqbnjitkeu64QqbnjuvjlAnmIKGsBkEQBEEQBD2WUFaDIAiCIAiCHksoqz2HYd3cPmQ+PpmeOq6Q6bnjCpmeO66Q6bnj+jhlgjmIkrNxEARBEARBEPQ4wrIaBEEQBEEQ9FhCWQ16DZK6FLHIWxYEQRAEwdxDKKtBb+LBisuQtEDZq5vHOceRdHPm/RFzcizBnEVSX0k/mtPjyEPSSlWWBUHwySasUnMASd8s+97Mri2Q+yIw2szel7QP8DngDDN7vqQvAXsDK5vZiZKWB5Yys1wlr7uRdGjZ92Z2Wo7MEsDSwEBJawNKXy0ADCpY1WN41ToBnwLeS+8HAy8Dy7cz/jpIGmxmk2bTur7ZOC4kLWxmE1qILJV5vyfwm9kxjoKxbWxm99eUWd7MXmizv68BnwUGNJaZ2YntrKtFP32BJclcJ1uNWdIywApNMiNL2s8L7AKs2CSTuz2S+gBjzWytShvh65ohaWfg9FZtJU2gpNqjmS3SQn4V4CUzmyJpC2Ad4BIze6dA5Br8OpblL8AGs7GPyki62FJJbkn7mtkfK8gMN7Pt0vufmtnJszqOCn3WPc4WBw6g63G2f4t+FgZWo/O51qUfSc2/YSfM7JFZlZH0V8qPzZ3K1pfWsSmwmpldlPbJYDP7dyu5YM4TyuqcYcf0fwngC8A/0uctgRFArrIKnAusK2ld4AjgD8AlwOYlff0OmAlsBZyIK23XABsWCaST+EjgM3S+SG2V0/Y98i8gchFrtmLOn/6vkcZwY/q8I1B0sf0asD+wbNqeBu8BP8sTMLPl0vh+B9xiZjemzzsCXyrop2PwNfZBCY+ToxQnhfsCYBngZuDIhvIp6UEz2yhnXcfQcVzcQdcbfDNtRU5KOsnMjkrvtzWz2yqI/a4xHkn3mdkmFWSuz8hcY2a7VBzfefgDypbA74FdKbCuZ2S+CBxPx829cWyuXCJzMHAc8Dp+/oDv03VKZH4N7IH/7jMyMoVKBHADMBF4GJhSth34oGdKGtOGsv9PSWcDVwLvZ9bXrEQshu+f44A3gEvT570pfjDMcg0wRNKq+PXpRuAy4KvZRpLWxB84Fmx6eF+AzPk2K3009bcxcBbwaWAeoC/wfs71ad3M+0OAlsoqsHjm/W5ALWW17vE5C8fZ3cDtGZlW4/oevg+WBUYDGwP34feSZv6vZFXWQmYAMAQYg2/7OsADwKZN7U9N/7+JP4z/KX0eCjxX0j8Ako5L/awBXAT0T+v4YivZoAcwp0tofZJfwE3A0pnPSwPXlrR/JP0/FvhudlkFmUczy8a0kBkOfBd4AleELwR+PZu3fTgwf+bz/LhSWSazexv9jKqyrN19ABxa8Pox8HbBuu8BvgIsBByGW4FXaf6dmmQezXtfMv53cOX2usz7j16tjpcqx1a7Y2tXJrUd2/R/MDC8hcyTwPb4w+GijVcLmWdatcmReQqYt6bM+DaO6X/gD2p34IrajcCNLWTuzHn9o6T9AznL7q8wtsb15nDg4KLfF9gZVxjeSv8brzOBL8yOPppkRgGrAo/iiup3gF8Wrbv5fZXx1JGZleOzzeNsdBvjGocrkqPT5zWBK+uup0I/VwBrZz6vBVxc0n5klWV5+wBXhrPXnrGze3vi1T2vsKzOWVY0s1czn18HVi9p/56knwL7AF9K05T9W/QxLbUz+MhiOLNchEXN7A+SDjGzu4C7JN3VQoa0/iXobIkssv4sD0zNfJ6KT1HlrfOHee8zfZxZMqS3Jf0Ef4I2fN+1mkKH6vvgJOAUYHrOd0U+4YPN7Jb0/lRJDwO3SPoWxRbRgZLWT+sckN433CGwrhayrKXy7IJ1zi76pOnCPpn32bG9nSNjBe9b8UH6P1nSp3Blp5WP40Qzu7lFm2ZexC2edXgWPx9bWkgz3CtpbTMbV0PmhHrDAjPbsq6IpD2Aq8ys8b4K0yQNBfalYwapyzXKzG4AbpC0iZndV3NslfrI6fMZSX3NbAZwkaR7c5otK+lM/PhtvM+uo8v1B1hZ0o1JpvE+K9Nqerru8dnOcXaTpK+a2d9ryHxoZh9KQtK8ZvakpDVaCUlai64zUpeUiKyZPf7NbLyk9UraLy5pZTN7NvW3Ep2t20VMTcdy4144XwWZoIcQyuqcZYSkW4HL8Rv2nrjFo4g9gL1wq+pryf/0lBZ9nIlb15aQ9Et82vSYFjLT0v9Xk3/gK/hUUCGSdsKndT4F/Aef0noCn+rL41LgQUnX4dv+DdylIY8qF6Ii9sJv7o2bwUh82qgVVffBI8D1ZvZw8xdpGi0PSVrQzCYCmNmdknbBpzeLfAJfA07LeQ8502xmdkdTh/3wKdBXzOytgj7Aj5ND8Rtv4312vV18ioEF8WnshoKaVZwNyJvOXFfSu0lmYOZ96qbL9GyDmyQthB/3j6T1/z6vYcYn7k5Jp+BW5Y9u8DkKftan+ln8/Pxbk0yeT/VZaRyTgdGS7miS6aLgSBqXZPoB35H0bJJpTAEXuhukh6dKSFoWfyi+J7N9g9PXl5nZMwWie+HT5udKmgncj7sCtOI7wEG41fLfSZH4U3OjzD4jKZ6dKFAKa/XRxGRJ8+C/z2+AV4E8ZeXwzPtRLdbZYOfM+1MLWzVR9/hs8zhruGkJOErSFPzaVuSmleWldK5dD9wm92d+pcU2HQdsgSurf8ctxvdQfG0HeELS7+lsUHiipP2P8HPz2fR5ReD7ZeNKXCXpfGAhSQfgrmUXVCDSVjsAACAASURBVJALegBRFGAOk/y1NksfR5rZdSVt58OfdmdIWh2flrnZzKYVySS5NYGt8QvUHWZWdiFA0g64f9Ny+A1rAeAES36fBTJjcIXpdjNbX9KWwFAzO7BEZgM6/JJGmtmjZeP6OKm6D5Kl4S0zezNnHUua2es5y/cCnrWmoKT08PEzMztgNoz/HOB3ZvaYPPvBvfj050LAIWZ2VYHccWXrNbPaVr3uQh6cNKCh9Od8X/bgZ5bvg122/WY5gU+S9m0h0+VGLWmFEhksJ2hS0neBRczslPT5Jfy4FHCEmZ2bI3M58Gczuyl9fgqv3jMIt2h1UUDTTMwPWsxYFCJpILC8mT1V0qZsn2EFgU1pbH80s31qjmkFfOZqHlzZWRA/P4qU9azswsA7VvFmKak/PpX9spn9p6RdreOz3X02O5C0Ob7PbjGzqSXtxuF+v4+a2bqSlgR+b2Y7lsgMAP6LjliCkcC5ZvZhicy8+P0P4Ekzq2RllrQtsB1+ztxq1Xzygx5AKKu9iDRdvBmwMG7pGAVMzrvhZGTyLHXvtVJw2xjbKDMbkpTW9c0DQYqChRoylaKtJf3YzP5P0unkTBmbWZcMAxmLbS5mVpqRoSqS+plZngvA7JbZEHjRzF5Ln7+NT/U/DxzfPNUu6TEz+2x6fwiwtZntlKbObzKzVgFadca2An4zn5g+bwl8HQ96OCfv5iZpEDCtcRwmpf+rwHMtHtgG4f7Ay5vZAZJWA9ZoKGMFMh9NGZYta/p+NzO7utWypu8PMbMzWi1r+v5SM/tWq2Vp+UPAVxqWcUmPpgfDAbjfbpfAQUmPZH/rhkx6f7eZbdYsk767y8zKAjeLtmdH3Lo4j5mtJJ/OPbHCVHidPm4FdixTmgrkqijRx+KuD08mhehmYD3czWcvM7s9R+Y84Kz0YLggHoQ0A58lOczMLq8zzgrb8ZHRIn3ui/uwTi6R+Qbuo9w4RxcCtjCz61v0VSt6vnHNT/eqLXHf6vGNa9GsoDaz6GTkVwJebSjB6XhY0syem9WxBd1P5FmdA0h6T9K7Oa/35NOhhaLpgvRN/OL4DYqn2Rs8gkf1/gt4Or3/t6RHkmUzb3yrS7pD0vj0eR1JrVwH3pE0GH8q/rOkM8j342z0cTBu6bgNDzT7W/qfx/9L/8fjwUjNrzzOBs4BXsJ9dC9Nr+l4gEIpNfbBgxmZs1qtdxZkzif5+Er6EvArfGptIvl1rrM38m1JmQTM7BUy/qTNSGoogMi5UNJESWPlfrJ5XEWaUk3KydXAC/hN/ncFMreQfJTlUd334e4C/yPpV0Xjw4NwpgCNjAMvAb8oaQ+eCqmZQqUz8dOKy7LkWb72ayHT6fxNikdR2qY+TS4cVwOkm+/AApnmyPqtM+8XLRnX3ZLOkLRJOvbXkVTompDheGAjPKgPMxtNjk+xpMUkHSfph5IGSzpX0nhJN6TjoYzn8OwGP5N0aONVJpCU6NH4cYek9dTkW5rYg47rw774ubI4HmR5UsHqNzOzxnXoO8C/zGxt/HdsmeNY0klJeWx8XlhS2TF9B51/74F4lH8Zx2VnIMzTfJXOoshnGY6k47hvRM+XMSptywW4a9AjFOfCHpeuK7mvHJEdS147tBgX+PmSjdeYQevrQNBDCJ/VOYCZzd+6VS6StAnuO/bdtKxvC5lbgOvM7Na0gu3wSPSrcEXi8zkyF+C+W+en8Y6VdBnlSsHOwIf4FNve+JRRWe7LQ3CLWJn/JKn/69P/P7Rqm5G5A/yCm7U4SboeqOLzV3UfZBW/qilQ2pHpm7Ge7gEMM7NrgGskjc5pP1HSV3Afs03xHIsNZahIsQH/XS5O74fiU3orA+vj/s95lriBSQkG9ze7MFnC++AKQh4Lm9nT6f2+wOVmdrDcr/Bh4CcFcquY2R5Kfo5m9oGkXOVbbaRHkrQ9buFdRp2Daxag4OErjWUvYKUmBWh+PAAsT+anwFF0+OuCHxdTyX/4AD+nPsLMTkrr6kOx4vmepNXN7F9J5u0ksyZQlgO4YVXNWuCN1mnfppvZxKafJG+G4zJ8Zmg1XJm5CDgDP75+j/s9FvFKevWhIxVeK47HlegR4Eq0pBVz2k3NTPd/GbgiWTCfUHG1vOYHw8ZDxGsFh2Yz21tKF5fkJkj6KsWxBQMsk7/ZzCbJZxzKyDNMtbr/fwM/7x9J/bwiqXR/m9l/p7fnSboFWMDM8hRPqKZgZtf9nTrtc+iXtcab2dR0vQl6AaGs9i4OwZ9yr0tTTitTHpAFMMTMDmp8MLPh8lyah6ZprjwGmdmDTRfa0mlrM3s/87GK71TtaGtJt5HvBrBdidgSklbMTPUsT7WArar7oB0/mnZk+qrDfWBrIOsLnHceH4Rbl5cCfmwdWSe2IVmXCpiecRHZAU+2/hZwuzwwJY/sTtqKZIlJriBF/WT3wVakQMF0AynLVjE1Td81gnNWoTgqeo20DQvRETUOPjVZ5Bf8Cq5E7YQrzVmZoipQ9+IBO4vROd/ke0Dujdo8cfzJkk42s1YW2wbDJf3CzJqVmBPxVGt5HIcHpf2SjsC3DXBF+ZCijorcAyowXu6T3TdZ6H+I759mljSzo9KDxvOW/HCBJyX9oKwDa89vOk+JzmOKPJr9dXwa+7DMd0UK4TtyH/eX8YfP74K7+1D+YNigrzzafkqSGwgUXZsB3pf0OUsBWPIZsg9K2oNbPE/DZ5sMOJjOx3celaPnJT0O/BlX7v8fQKvpdcv4Zct9Wxu5vx+0cl/fBfHjuvHgdBfuatLqfvKGpJ2sI+f2zkCXWIOgZxLKai/CvHLIyMznZ/GbQRlvSzoSz2UHbpWbkCxsRUrBm0kJaFykdsVvxoWoc3GAefApo7yk2w0qR1tnyN6kB+A+m60c63+MT2k2pvZWw535W1F1H6yZpqwErJKZviqL6m5H5nI8fdab+I3p7jSuVclR+s3sSWAbNaUGMrNbJZVd1GdKWhpP77U18MvMd0U33n9IugrPUrAwqchFWk+RX+FYSafiN/hVScpWdjq0gONwZXs5SX/GlYP98hpaG+mRzGwMMEbSZVbRrzvddJ+nwzWhMmb2U1WsEoRb+n8v6Rk8gTq45XsUkJt5wsxuSVblI+i4VowHvmlm45vby32aV2jsL3mquEb2gCusxM83cTBwNH5eXgbcSv6MzIw0PkvHdJbS1Hpy38kj6FrFrKxgR1Ul+hDcbWRx4PSGf2aydBYFgH4fn3VYCvhfS37l+Pnzt7JtSfwJuEPSRfj1Zn/KH/gPAa6W1JjNWBq/rpdxMF5A5cr0eTits8LUiZ4fimezGZ5+z8tx39/S7AEAknbHH1ZH4NfAsyQdbmZ57jvgOa/HA7unz9/CLfOt4hAOwl3Uzk79vAh8u9X4gp5BBFj1Itq5SEtaDL/Bb4qfoPfgqZwm4sEGXaJhk8V2GF5dawLwb2BvKynrmrOOrwMbZae3mr7P9ZeqazVRhUCQZKn4TPr4OG4xKK3iUnUfqL2o7nZkVsKD0ZbGg2neT8tXx4MeuqRhSt93CrBJyx42syJ/5R1w14e+wF8tZSaQRwMfYWZfy5ERfrNcCrjazF5Oy9cHlmi4oDTJDMRvukvjbgNj0vIv4FP9lxb0syyeumdj/Hi+33IyMTTJDcCtXc3nTWGpyaTQnEzXfJFlVa+qVknKyuRWCWpxTq9Mh6/r4w1LVhmS1rcK2TbSA8CVGevTv/AqUYPw36VWFH5JP+/gD97Cp/4byrmATc1s4RLZ4bjSdRiugOwLvGFmR5bIDMKV6MYszK3AL6wg4lzSgObvJC1i+TmDZxm5y842+PYPzztnUrs++DHyED5zIDwavvDBKhkmfmVmhxe1KZGtHT2fzoM9cGPCM7iLT2GKKHlQ7rYNa2q6z91uZusWtB9tZuu1WlbS32Bc93mvSvugh2A9oDJBvKq9+HgqS/UhVYrCg2bmn4V1Val4Mz+ubFVZ3wKZ10K45eJfNcbzJeA84LXu2ge47+A3gA1mpwzwcPp/R8V1boQrQS/iVqTG6xhaVG3BFa3NmpbNV/Y74YrZ7bPzWCzbDzVlrgZ+jgfq7ZvOozNayNyTjq+xeM7g4/HUZWUylaokNcnUrhKEl87cC5ivxj64E6+U9HPgsyXtHmn6nK32c3eFfm4DFsp8XhhXcprbbV72qnIMZI9j4K4Wx+YpNY+Zv+E+jo3PS7c69nBr7FH4Q+6FjVcLmdrnDf4wU/ccKKxWNrvGlbOOLdK5MKVFu3FNn/s0L2vefvyBpvH5i2X7BNgn/c+tNjgr2xivj+8VbgC9i9qVpepaY839DP8Hn8J5P69NQT/ZKZg+eA3mQrN98gu7lJQEP00dfds6omrzeIyOBNfTcWtnaU7S5M+1F/6UvzgdClshdfaBpJuAn5hXXVka9wschU/vDzOz384OGbwy1HHA6sqJfLau7hPz4T6U/ejso/seXr+8EHO/0d+QmdZutR/Mc/9OVqbYQRVUsy46cL+kDc3soap9AKua2W6SdjazP8oD5XItVxkGmtkdkmRu6T5e0t20iKC2alWSsrRTJeg03HJ1sqQHcSvjTVaSl9LMtpS0FD51Okyee/dKM2ueom8OPMv6gy/WYlwAi5lHmjf6nSCvatc8nrvUZs5UahYtScdmUYaFIq4H/iIv1rEcXtL2sHIRbsDdc24nuTm0os3zZnga17VmVnVq9FF58N/VwEfnshWke5qF83lD3CVgFzxrwzBaR9zfoo7iOODHdlmlrYOAS+S+qwLepjzrRsPXtt3A5qAHEMpq76J2ZSnc6f1KPNDkoymzFjK3STosyWUvbGVTYNkAlun4hWrn/KaAX8QONbM7ASRtgftDfaFIwMyWazHuj5B0An7Rex2/CG6IO+5XzShQdR+sZB2+f98BbjOzb8ujZv8J5Cme7cjsiecu7UeFi27ar3dKusha+xnm0c4N8UNgnDwQLrvPyvyq/4AHLj1MtRv8lsD3JT2f+mhZ8YmO8+ad9JD0GgWlfTN8mKZcn04PLi/jtdvLqFolKUvtKkGZB9W+eHDaAbgVr6waEea+lGfKk9EfARxLV3/SSZJWteQeZGZvwEfuJlUeXmdKWt5SvuTk8pJ7/CSFaHFJ81i9nKm/SIrKj+ko2FEU/NagrrJ2QfotrydVSDKzVg8eg6zEFaGEuufNofhxNUPSB3ScA2W//yJ4ZoqskcJIKe1mdVySTiLFQ+DxEV80s5dK1t0xCLPDk7Gj4ao2zEpyLZu7DK2bHrgws7J0j5jZ+elcedfMTq8ypqDnET6rvQi1V1nqYTPbQNLYxg1dLfw8JeUlfS6zdtVG0hhr8knKW5b5bhm8AMIESUPwC9szVpAMXtJbuCX2NODvyVr4bNVtqLoPsr5S8vKHF5jZFc3fzapMRnZ7q1FHXF7S8Sf4DTdbfKG0KIA8YG4+/MHjQyrcEFVQYcdKKutIesDM8tKnFbXP9fe1En9quV/oNcA6eCDGYLxS2PklMhvi7jYL4VPnCwK/saaqYzlja6tKUpLfnApVglLbgfgD4h54eqmbzOzgkvafTm13wyOgrwCusaaoa3kg0Wn4NmezB/wMf7gsDRiS+14OoyM93JeAA63YB/P8NP4b6awQlQVa1kYevNSMWZPfctOshfDgnXGk4Kqycclzo95rZmVWwTy52ufNx0HBuMzyK7Idh/um/msW+lsM919+wfLLV++Iu348nz4fS0dhlEOspFhBan+nmW3Z7viCOUsoq3M5ku43s43TNMuZuNXmL2a2Ss31FFo/5ClAjsADS8CntE80s3uKppHkFaYewV0BwPNzDjGzr+e0PRq3Hs3EE+F/Db8ZbgQ8ZGY/zpHpj+eTHYrfMG9Ln5cxs9Jo4yLy9oGkv+I+kC/h1q2VzOydpEyMspzKLe3IZGTnxS/QK9JZ+czNaSvpSdyPbhyZKGurEJTzcSAvANCXFnXRC2Tnw63Ne1lO4FdvQh4A9Bk8jVPpzIekK/H8yLfg+ZJHtDqmJd2PzzBcbS0itCWtiyeDbxyH43Gfz6Kcuc3yi9ERAHeflQTAqUagZbJWP2tm5zUt/xGwVF2rpnLcSYrG02JcjUwowh/wpuDW/CoWz8Y65gFWTx+fstYltHeiI3XTiKKH9kz7ZXEDxxfTWO/BFbxK1s+0juWAPa0jzVhemx/g5X3fSZ8XxstudykOomJ3qJXxB/jfNrUfC2xsZpOT4eY0/Pq+PrCbmX25xfh/iT8MNs+WtbzWBHOeUFZ7AfIqR4U/VNk0a4E19ngz+2uFfoVPu+6FlzdcMqfNf+MpTY7ALzTg/qq/wJN8H5VnLU0XsRNwCyl4NPAJZjYhp+3j+AVpPvwpeikzez8ppKPLlLskPwjPmzkUv8kPN7NKKUta7QO5P96JeADGOWbWSMG0JR4wdWrOOmvLZGRvwTM5dJo2N7P/K2j/TzOrWnigYYktJO/CLq8HXnZ8Fk7RK78+ulmBT3W6qX8V/z2+gltMry06npO1coJ5UYfd8Rv8M3jt8S5pz5Ki9QN8OvNCPKXOZnhw1o/zrKTyzAFH475zp+HuLA2Z7zUrRElmJ/zh8W3ch/oc3Cq7InBkC2v0V3DXkUp+kRm5efAALsMVorIa72ub2bg668/ItkzFJWl3M7uq5nofB9ZqVszl7hpjzWytCuv4DO5SMxSYaGZD6oyhO5C7QP0Rd50Sfq3et3mfZdr/Cndr+nNaNBQP/ioqpEGayr+MzsaBvc1s2xZjWwy3xg8FlsFzfBf67ubNDClT4rdpebYk9FHAmpZxh2q+bmRn3iRdiB/Dv06fu2Q9yemv1rUm6GFYD4jyilf5C/czLXy1kP1ilWVN338eVzRfwKvc7ItXHMpr+wSwSM7yRfF8oP/VtHwAsHhO+yXxyix5fTya9z59fqRoOwrWtRDw3QrtKu+Dj/lYGF+z/XZ4KqrdcIV9J2CnkvZ3lrxyI4rx4KjC12za7m1x5fFlPC/ljsBzLWTOwR/UHkoy15OCM3DrT57McLys5ll4mrPDcQXvANyClSdzD16k4bA0vt3Scb4t8ECBzBjckrZhOr5WTsuXoCQSOrUZgPstXosr6z8qOncyMl/FM0OMwGclXsArJxW1vxt3ozkOVyKq/k7fw634E9Ix80HecYOXVr6lsd0V1/1Ym9+tgLvCjMEf8t4EVmzRV6WsBk0y3wAWzHxeCPh6he16GK/m1/i8OiWZB/AMFX0yn/vSOsPH6CrL0vL58fyjt+D5sP8PeKnibzSWZATLjC33t8n2j5eQ3bPFeMfiLjx9cKPFkMx3j1c9juLVO18RYNU7uBJPn9RpejBZ6Eqdy/GbbvMTZ96yxjTJ7viN7HLc+jfKWvhOWU7glZm9Jel5Mzu36asz8Ytgs2P/NriVNS9h/4LJX6kPsECySoFbIRbMaY88mXlt2t0H8gCUw+g6PV+WL7O2DHBvTavX3riv5mA63AAM9xHsgrXn07W0lfhy5iFpHzP7kwpqultX38BbcQVqU+tI1H5Gi262NLPPyPOsvoznfJ2R/CSLSkC2U1lpsJkNS2M6yMwa0c+3SSqaMp1pyb9P0r8tBcGZ2X8klVaLw5Xt9/DzGNzqdSnlWR5Ow/fHM6nPVfD0TLn+z2a2mdxPfA/gj8kqe6WZ/arF2A7BFfD7zTMQrInPoDSvfwd5Lua/ybMznEtnN5W8YM7JklazjjK9pG1ZjYIKTvJsDAviPrq7mtnTaX8/12I7FrcKWQ2aOM4ygUHmrj3H4Q9JZfQ3s0bREszsX2nWqIyFcKs8FFwDm3hT0j50RNwPpaAUMPAfvATuMcA9ZmaSvlGhD/Dz9CpJ5+HXmYMorpj3oqSDcXeozzXaJXeovO3/LZ6L+F3gCTMbldqvT0nRGkmfx/2oV8EfpPY3sycqbk/QQwhltXdQpOBtS4GCJ2kTPLJ+8SaFYAH8aTePA4Gn8BvHTeYpdVr5ibwraV1LSd0z/a9LfjnVTc3swOaFZvbnNBWUxz/pqFZyL51vykURuo10Tavhvq2NaeId6Aj+yKOdfQAeZXweXte86vRsOzKbAvvJA8CmQMto+A2swvRoA3kp3qPS+22tQhJw4Hekhx9J95lZlUpOddPJbIBP394u6Vlc+Sg6jht8CJB+w+ctTZunm2+RT2A7lZWyy5sfHotk+qTp8j54BP3C8FHZ2rw67lnWsM6uNXfKE6uX8R/r7MLwLK6UFGJe3OE0STfjJXR/DrRSViun4jKz69NxPBLPH904zwz3W2zmWOBmeSBTIwBnSBrb/xaM5w08Y8qS+DXhaUpcVjLMUMWsBhnyfrcq99hRkv5AxxT93pSXQj0Zz25wJ37MfIlU4riE/fHyy6fj23FvWpbHUfi5di5wmdxHuipH4tfQ/0pjG45f3/L4Lm4M2AbYI/NwsDEeCNkJM7swuTOshM9mNHgNz6pSxDm4UWAkPrP0W6DUvzXoeYTPai9A0uNm9pmC7z7y+2lavjmelPkgXCFq8B5emejpHJm++LTxUDzFyZ34hWQ585r0ef1vivtOXYRfYA23rOyLJ2O+p6n9E2b26S4rav1dX3xK7Zq874uQB5btZim9iTryS25f0k+tfZDkCqtCzWaZWtHw6Sb4m6zlpsX6P/L9quIHltp95JNW5J82O5HnZm3kchyN+9ENy2n3Em5RFD5V3rDWCi+L2SUVmtqorCRpMu4HK9x680xGZmUz65K+KilpjaCcZszKK2VdDJzXsGYny9G+ZvbfOW0b+Y+3xafDr0r97ob7/HUJTkxyq9GRPeA9fHbnL2bWquzydbji8L/4+TMBtxx+tandvLjlblfgcGsRIJSRWwt3zWg8gI0HTi2baZCnudoFP2ZWxa2SXzazB0tkamU1SDIXAu/gypHhJU4XNrP9WmzTvLifdCN100g8i0RhKWl5QNKGqf0D1lHitaj9Ytai0luOzMp0lFFdDXcJuc4qRvxLWgRY1syKZjGa28+PH/uTWrSrdd1svo5Vva4FPYtQVnsB7Sp46fsVihSZFn0OwK2QQ/GL6B1mtldB26WA/8ajh4X7up2TdwGVFzE4vPlGIU8V9H9m9qVmmUybu81ss5rb8SSwjqVgknRjGGNma1aQrbMPjsctVdfROaq9MDdtHZl04c9iwDvW4gSWBz+tjitQWUts7sW6TWV1DP5g1Af4R3r/kRJWsD3DzWy79P6nZnZyq35y1tEHV8L2NLMulhW1F9m9eQuZLlb5ogeIjExe+dxNzbNldCnrWYQ6Atn646U2X0ifV8B99rpY0JWfsikztPySs5Iewq3XVzesi3VRSSouSU/h/rY/N7PcKfwK6x/cSrHJkVkCV8KH4g+ghbmbVSOrQWo/H57ia5u0aDhewawwP22awl4F9+ssnZpOYz8KV7jHASdbixyjcvepC/EUdDPwynyt8sXmrWdtPKhxdyvJJCNpBG697Ic/SL6BVxfLdfdJMtkCMUoyhQViJJ0DXGwVC4OkmZhsUNip2c9WkGs36FmEstoLmEUFrx1fypUsk7MuWSN3MbOyG18lJG2EW3cupvNU3rdxpeOBEtlj8GCU5tQjhRdseS6+b+A3RtL766xr5Z6sTB/cv+2qzLIFgG9Yge+q2shNW0emwBI3GA8a+Z4V+ODJfRPzOslNXVVijWzIdckzKek5fLq7spWwyRpb2dIhT+x+BXBDmRLQLpLuMLOtJf3a2kvwXrWfRv7jOtteWylusb7SSmByv8nV8OPu6RYzC80PU81j6/TAIukzZvZ45vN8VX9PuYvTH3Bf4eXlLkffz7Ms58h+1E/Zg7wk4dPxK5vZiZKWx7OQ5Fpj04zMr8zs8CrbkGSOxaPyH8YDOk82swtK2t+S2o7EH6Lnr2C1HYsrmE8mC/xvrCTH9qzSOK/luY2XM7PjlMnxXSBzL3C0dS4Qc5KZ5RaIkWeFWB0PsmpZGKTdB7agZxHKai9gFhW8MbgbQHOqo0KfqLwbaNHUi4rTFhVeQCQtiVtiG5agx4CzrSlBeY7cizmLzcyWbyG3IT6NZ3h985ZP5JJGlj0E9BTSNO+BZvaVgu9XBF4xL4qwKR5s9aciBb+FNdKsIJ9rXdqx4Ka2m+OWsa/hQSCFpUYlnVm2LsuvxPM47m93Hm5JUpNMXuquRp7NLl9RkGdTnvf0ibQdV1QZWx6qmWtWFVM3SfoynoLrhbQdywIHWEqzltO+LbcGSV/AfRorK56SHsDdB27MPPCMz7Mst9uPpEbA11Zm9mm5T/FwM9uwpI9/lBkBcto/Bmxonjd0UdwCXbb+Tmmhqpw3zW0qymSP58bv2fhtc4/njOw43I3qj7gC+lAFZbVSgRhJf8fvG7lKS92HtaB3EQFWvQAzezAprD+gowbyeODzrRQ8YLp1jcjPRR65+1k8+v6bma8WoGvN8AY7VFl3FjN7nRY11gvkKpdbbeIDYDJ+kZtcUaZSuVVJW5nZP5r2F5n2XaaY2pEpwsyuTRbnIq4HNkwW1kvwCPDLKPjdLE2NS/qimf2zady5+VrVRm5WYOVkJVXmfVZmpxyZxjT8XapWarQsSKWIY/E0R8vSZFXGj58uyoiZtVNzfAd8ungrao5T+blmzytpvwKunA7Fp4NXwNP+PFfSzRnANtaRsWB14AY6Cn90wsxWqrMNGU7Hg11uTOsZI6nlQ6KZvejGz49oFaBYt5/Pm9nnJDUqV01I+72MWiVd8WC0yanNW2lGpwypcyBe3+zn5mtTYgl1DrDt9DlvpqTN47nBiXhGgHuSoroyHtRWxrOSfkbnHLB5s04X464Vf8QtxKWFE5pJRpKTgE+Z2fbpwW0Tq16CO5iDhLLaS0hK6XHpgvlp/Kn/nXIpAP4qT9xfxZdyDfwmuhCex7LBe7hSkDeuulOPtS2xTfJr4pV+sgnHLytp/z/40/h1qY+rJJ1jORVVmmhMDWXTFeVFKW+O+2nuSFeM/Nrb7cjkIqmRd7CImWY2LSnGvzWzMxs3QWYidgAAIABJREFU4BZUTnmG52EE/02G4K4Jwq24D9BR+CHLzpn3hUUQ8lDXUqO5rhnWRrlKM/sL8BdJPzOzn1ccT60p8LTsTeAKuc95q0j+Rj/b4grnl/HAv0uBjSzHXzcj027qpv9YJpDGPJ1SaWWtTJ+NOu+NmYzS1E1tKJ4vJkuppevhD3ErdSk1+5mWHogMQNLiFGd2aLAIng4q+0BTdj6vknlIU9PnvAe2BfEHm+xGNB4EizIoXEDnjBvNn0tJFuhGnMBIaxEsZZ627erM52fx4LYy9sfTm10LHwWYdTmmzewqSX/DHyhHSbqUzunOWpXovRgPBD46ff4XbowIZbUXEMpqL0Jet/t8vDKOgJUkfd/Ka8Xvm/5nfalyL2xmdgNwg6RNzOy+imOqOwVa2xKb6esYfIppTfzp/ct4CpNCZRVPo7KRpUAMSSfhaVtKldWqliIzOy79L0udMssyys9HujAezHB2ieh0SbvhNc4bpWwLcziqjZRnlnKzSroCd0kYlz6vRefAhqxMWfqwQtS51Og5lJQalZe1LfRzyrPeZqzEf8uzGBdYiRtZMHKnwMk515SpStekQDX6yXMDaCfXbK3UTerIYTw+KU7Z7AGF0fMZ+d/hAUCNfJ4HyVOgFeWobUfxPAi3/C6D5+gcTueHytnRz5n4A+4S8tzLu+LZCwqpcz4ndm76XPrQZmYr1lx/bhBhVSQdghspGsr2nyUNM7OzctoeYWa/UUG1xTK3FvOqhVXzYk/Drdbz4kp3ndLZiyWF96ep3+mSalWBC+Ycoaz2Lmol9oa2p+delKegaVlHuu6U0Sz6Fe0BrIdXrfqWPH3L+S1khF/gGjRqdrck3dxWpHNg2iVNbS62FOQgad8q1rx2ZOhqDTE8v+A+Vl4gYH/csvwbM3tW0kp0KBJ5zIMHbvVr6vNd/IZdxprZsZjX/F4vr2GJhb0hW2Rhvwj3z6xyk6llsU3klq1NFLkBtHOOjWrdpAu1c82a2c7qSN10gqRVgYUkbWT5wULZHMYT6chH+R5eXasVm+MlURuK+B/xyPUiaiueySq9d4WxtN2Ped7nh4Gt8evF1611tP6y+OxDy+tm6qOtB7bU1zK4O0f22pRbnjW1XxxXPFdskikLLvou7g7RCEj7NXAfHcUosjT2TeXjutn1p5nmh0l5OrHTcFeOzzVcKGrwvtw3uHFsbkx+LvCgBxIBVr0INQX9yE0yd1l5NoD+eMBIo80I4Pwyfx+1WUc6yS5B5yn6F5q+rx2MkpF90Mw2SjeRLfDMAOOsPLDiCHzqNJsN4HIzK1Vk0hTTKnj6lYZiZM0WArUR1d6OTEZ2N+uokFS4bFZRJlI6+dINttZpci7HrR5/wn/jfZLc0Lz1l62r+aFGs9HPt7so8oEsUyJmoa9KuWZz5JbEH/r2pEXqphzZ9c2s1IVE0rXAjzLHzgp4lHyXY6BdlB84NxGvNHfDbOpjbXwGB7xa0vgKMrWum+0+sCWlcQ+8HHD22pTr551k7sWt8s2BtoV5q9P4NrQUvChP5feQma1dJFOH5FbyIv7w/AB0CWa8q6n93cBBVpDSqkJ/n8MV7bXwmI/FcdeYSnlggzlLKKu9ALWZ2DvJ/h6f9m1Y774FzDCz75XI5EVidopEzZHZCbdKfQrPHboCfpHvUrCgXeRlMo/ErSo/pKPs3rdbyG2I+10J97uqkg3gCeAz1uIEUXt5SduKhC9qX7BsFTxQaAJeseV8/IHlGTyqO286Oyt/GW6NmoHf4BYETrOO8qN5MgPo/GA0EjjXKuYRbTGeE8zT4OSlobEyC5E6ItWbhQrTiiW5tejqH31JSfu/Zj4OwCunPWzlaeIWx4/p5n4qRZWnB4lt8KwgtVLwqEIOZnlg1Z54MNeHZdeA1P4uPFl9w2q7IW6NawQSNVvLaiuekobhimTjAW0XPKPIcsCzZtalmlXVfpIV+oa0rrH4NWNtPCvCzmUPbHnXyLLrZuaBrWHhzVawmmwFmTfkOWrXsZKiAVXGVkHmUNyNrOHvvzOe3/S3OW1rWUmTTF/8njYU92//G25IaEsZrYKkfnhshvB7Z60grWDOEcpqL6DgBt2g1Y26UlqQpu9vx53Rs3Wkv2NmW5f1g0+R3m6eZ29LYKjllFZtkiu1xJbIrQos0ErpSm0XwP32stNfpU/Tkq4GfmitK/b8B5+OFW7t6JSGqNkSOwsy2+MR4LvjQQENFsCV6o2a2t+N/34L4DfDI/CSs5vhNcw3brFdo81sPUl749PPR+KKV2kAXF3SVNxZeNDgPPi09vtlFvacdezSwkK0aObjAPwhbxEzO7ZE5jjcev8Z4O/A9niEcytXiOw6lsPdLwqtipKG47/nYfjDwb7AG9Yix6ukdeg6pVtoXU5K5+F0nTruohSn6exGequ+uOL2eetcrrWon1pFFdpUPP8BbGcp72tSQIbjis84y6n2V7WfpNROBY6w5AudlKqTgYFmdnDJtte+bia5f5rZF1sty3x3M16Vr3JBBHmJ2nvN7O9VZZLc5+gIkLy7yLJe10qaIz8vvr9OAU60HL/YdimajcmMbY7PygStCZ/VXoDVd9zPMkPSKpaSwMtTibTy98urI91qDNMspV+R1MfM7kzTVbkUWWLx1FmFSNoTWMXMfilpOUkbWHnO2OPwIKusdc3osP4VsRjwuKQH6ZxFodlCkA1cq+qv1Y7MK6ntTnROdfQenry/mfktZTyQdICZNW6gN0uqUi2qv9yF5Ot4DtxpygkEypKmpo+nq0JUZsE8G1eMrqYjd/CqFcaX5XQ63Dy6YGZvNS36raR78KjiInYF1gUeNbPvpOnzohrnRbxERy7hIhY1sz9IOsQ60nK1urFfiFuiHqMjwKRVFomr8fRWF1By/ksaifumXon7Qz8hzx7QUlEFV0qSxXA1M7tdnrmhn5m9VyCyKp7LtKF4nktG8SyQWQaYjw5/w/nwdEQzJBVZG6v2sw1utcxGmc+QdFTJeBrkXTerWLvnU6polsb2hbRNRUwGRku6g87XprIgpUOAoyRNxZXxlm5XiRn4thjlwUxL0WEl3YuKVtKkpH4tya2IB7bNbuUxL+tKg1rZV4I5RyirvYhkYc2bziy7IB4O3CkPyBD/v73zDpO1qtL97+WQJOMFFAWUiwIiA5IM6HBRQB3FgEQZRTGBowImRjEhKMMgjyKCig4SVHAAlUFAopKDHDIICDPDiGkwAUdA4Mh7/1i76K+qv1hd3ae62b/nqacrfLv219XVVWuvvdb7RiBRG3im7Obgdt1+xHZyFfcpZJQuIbpG7yU0Has4mLAy7MvE1p2XpKOIkoatgM8T9ZFfJ7Yaq9idcKFpvWWWOLDNQU7NUaqoJR3hmBuBGyWd1HLrqvjFMthE0KaD9hjgbkKG6pIUgDQ1IxxLBM59dXFN2L5L0jxH09Rxivq6LtRG0erv6l+MCIqbGgMftv24pIUpM38v5dJAxXmKndCLEc2ATbJUvb/lbyW9lliUrNEw5sVl2cMG2uotLyA+I1Zk4jVqvf0m6d3E4vCpRM33GsT/aFV2cZjA8zAiWLuI+NtvBRyiMEi4YIrzPOoSpy5H53jTZ8hDZdvdLXgn8K1UguB0jnWf6WekS2s8hHaqJtQAvk+8zt9RhRpA+t89BzinkCW9SFJlllTRfLch0SD8WbeoCx6GKSZ7MmNCDlZnF2cWri9NNAv9purgVM/2MGGZ2KvTuX2IwA3gQ9QHq29Ic32QqLlakRCIrqJTJjaxpfuFuv+kZqHuW4kv3Va/cwqIT2ratirh4xT0BWvum+qYV0k6mInsZVWGZH1J16XH10vXSbfXrXl+iCc8kshyxCDpl4SpQB33u15GrYyH0t/wBkmHAb+lPqtURlMwVezwX0hk2XdpGDNf0kpEJvJaopmvSbqpmCVfSGSWLq86OPG5FKR8mCiHWIHyTHmRKzVgV9qCVnrLtl+r0I3dCfhXhc3oypI2bVNyQ5ScvJDYCsah61qnItA58EyZ6LPTPAIOsN37HKyyO207z9KSNmHyAkiEXNIkJL2OMKboSSHtYrv1givtDG2cFkWyXbsodEE9RGEKsGaLsqaefezatg9OJSqru8I+NtFFDWCYLOlbiYTDusA+hZ2btlnfzqQF4fPpLz0biStfZnrJNauzmBSMXlBWd1Y45krbLxnBXPe4onM41XSda3vbDs93AbHF/C/Elvu9ROdpqR90GnM18BKiKWJTRS3iBU6d9RVjNiNcnG6i/0u6tI4pZRN2A1YntkJPtn1DzfN3qiUddkxh7F3Am4javDq9zHWqHgPolYV0QdIvXWNtK+lQosbxB/S/1pVBTsrY3ktkzD9ILHK+OrjtrHoziXVtlwYSo0BhWbtCVUAgaS23rLUe0flsRdQf/454nRsNNRRNZoO4oUQDSc8g/h92A55mu1bFQdLVtl+kCY/4xQmpubpzW52JwPNnhcCzbp6ViUV4MeioVV1oM08KZus69F9eMuYmIkC9XdKLiDrl2trdgfGdnJXSOb6eWKzeQGjpXmy7TIu5N2YY+9jWagADWdLvTVeWdCpI+jqwDPByoqRnJ+J98M5FemKZVuRgdRYjaT3gLNuVNX6SPksEaj+oC25azNUUqJwBvLUpK1A4flkiE7sYE5nY75bUFxbH7EFkkzcnMhm7ENtHk7zVC2NuScfeTL/byYUN5/csJr6klyYaB77ngqtPOm5jYrv3IPprIBcAP3UIXg8+d+cxhbE/BbZxhRD+VElfvKUP0RAUpnMbxHWLqQ7n1UnqKo0ZRgO3s3Ws+tUdvm+7ybFnsGSgbJ7K+sO0YPkQk9/T0+KNnrJyywKrORyJ6o49jHDW2wP4AKHx+3Pbn6gZ0ynwlPQuogZzDSJYezFwZdP7bJgAtw0aUOMYvN1i/I9Jzkq2N04B/vVlQWE6vrcQeBeRVf2MpJsaFgTX9XalPCGb19RoW1QDgEguVKkBPM6ExWzxfT1tWdKu9F6jws/liO/FVy7qc8s0k8sAZhGa0CgVE6LwtV3DxJfassQW1V+p+fBQvQbqUxrm+Stws0JrsOiLXdbZPg/4j5SJfZwKu8zC8WcD/2T7RIXG6rbpnHZusYL/k5tt+CaRvvj/ldgK3YQIeD/DgAi7u9eSDjWmwP7A2YomnGL2su93lPRn6vVsqyxCn0YIwQ8GzCIaRkpR2OB+DrjahS7llEWuRC1lpWz/zxAZ/OIX8b40vM8SxZKBzehvZis1BaB/y7g2U1mgWDLwWeK91ZZf2u5UsyhpGeKzYC3b75H0XGA922dWHH8i8H6inGE+sftxKCHKXsfHiO3jm4G9CCWFysa0qsCT8te5x75EnfpVtl+e3nu1Tk1t59FwneOrqd/tre92i8+frs5Ki6cs8S5MWIc20dk+1vYXUxb3ZcR7fE9XqAHYrrN8HhceTj8fSjsGfwKGMfTILAJysDqL8BBF8l3GDPP8Bc5Klzbz/E3SQ5JWbJmJPR44L201HeZuOnzXKGo8z6A/uGuq8VoCeDWRWd0GuJj6L8S2taRTHfN5on5yaULqqYpVah6r40xCyH9S6UP64pqEpH2IWsXbgF5ne0+78vPUOKwRWfIeT8hKlR04xPum805CcZs3ZaEmbfs2zNNqTvfXHe7XJutb4HaFDu6P6H9P19UHHkcE3r0ym18RtdGlwSrwd7YfkLQ70TW/PxG0VgZeKRg6wfZbiFrfNnQOPAm9179KQtJSaft9vRHN0+scX414rX6Sbr+cMFQpe42/SX/D3uDtJro6Kx1EWO9eZvsahcLLnQ1z9Oxjn6YG+9i03b83oaBwM1GWU9csO1s4U1GHfhgTi9CuCh+ZRUQOVmcBaQv0vt4XtKJz/o1Et/bRth8tGfN+20el68/vGOB1xvYJCpmatWzf0WJI60xsyjqcRWyZz1e4SxW3P+syF736z62LT0mFdJWknvzK9kSTSM/v/sGy4wscQYta0hGMeWqbbSsP2JEqmmaWLtxVWhdYV79le/eKh94NbGb7L4r6ztMkPdv2l5ncqDL4nF1lpVq/b4A1FLqZKlynYUzfIQ2P99hY0gNpnqek69B+C7RrUP0UIkgtvg+aJHjWsb2rpDcTJ/Vw2t6vYsm0Hf0GwtjhUUm155kWE6tKWrLsM6mCYQLPX6Wg43Tg/LSL0FTn2moep85xSWcS9eO/TbdXB44ue2LbTcF1Ex8iFtPrSLqc5KxUdbBDQeTUwu3/InRjK3E3+9gTCJWKSwl94ecBk/RuZwsKU5h7bB+cbi9HBOG3EzJjmVlADlZnB6cQtZr3K7zWTyUak14AfBUoc6Pqaf5BOKO0rqEaBkVH7OFEtm/tdJ4HuVrKpXUmNvEYEZwsRWQtWtVs2v77DnMAHEBYJn7EA53SDdwD3NIh6Bx2zAWSXmn7vDYHK7pfv0Rsf/6RkPD5BRNWkqNgXm/r3/bdkrYmAtZn0RCsqrusVPF903vdquYYRs+2M7bnNR810vmGkeJ5NC0me9m7dahXyPg3wrXpFkL7dS2iprqJu4HLFTXsxcVE1YKyc+Bpe4d09UBFnfSKhGxSHV3nebb7DUH+lwYVjbS1/m4mmzXUaq3avk5hplDrrKSQBbvIobAgQipuJ+I1f1vVFn2BVQh5rePSomJt22WNdxs41ctKOpZmFYxx5xiidKzXnHgoUU/9AuAb1CwMMuNDbrCaBahQPC/pcOBx2/sr1ABucElhvfqbPp4oqp/Gc7yWqP+6yBMF/De7xke6bSZW0quJ7ccziAD4oQ7ntSpRS/lM29srOm1faPv4hnHrAL+y/UgKvjYCTrR9X8XxWxDasbW1pCMYs4CoQX6ECOBrs3eSbiDEus9zNGVsB+xoe++qObqicBT6ULF0IGXlvkV4o1cGc+pvylpIfPEePviekPQGYA3bR6fbPyMyUAb+2QN6tQNjS/Vsy8ZoovGptbvYMKi/PnwZkh0pLbKxCoeprwAvTc9xGbCv7V/VjNmO2PbdgNjWfynwdtsXtTxfAUs0ZUwVJhyTaJN9TAHbisA5VfOkz7ybbDeZLUx1nqOIZqyTidd4N+Au1ztYXUFkI/t0hl3jrpbG7ZzOZYGkTxKJhc95oJlP0Sy6icOgY3dC7uyVwCaEK13lwjz9XTYn6pTXVdRsnuoSlyxNsWFs3FChkUzS0YRD3IHpdmcb2syiIWdWZwfFzNErCC1OHKLlVWNWkrQDka1aQQONAw31bcOw0Pb9A+dTJ63UJRP7CaKZaphShuOB7zLRiHYnIRd1fMO47wObK2xdjyUC5ZMIyaky2taSTmmMu9cVL7T9e4WerWyfr6hZGyV7MGAAkWrc9pB0TN1At6sJhaiZ3K1we0miAWo5oh5zVHq28yuuj5Qh/o5FjiPeiz0Dibek+7arme98hdbui4nPk31t/6HqeIXm51sYyBISW9aVdNkSHww83ULbOH3m3agOcmFDzvP+9PnZKxf6hu0f1o0BlnGDTW4Fn7J9qqSXEc2NhwNfA140cNzCQsZ1e2Lx/Edit+Wwhjl2IILa6wBs/0ZS1XuwV9YC/aUtY9PZ35F5khZPn0nbEKYVPXIMNEvIf6jZwU8knUIIpq9MKvpPdVRVmY6LmXChuoR+y7npsJi7Ja325yk6jfehpnuccIh6IdG0gO0bJJV2Zg6xlV9kNdsnSfpoeq7HVN9p2+NxR1fuDsARtr+iZEZQQata0qmOUVia3mD7QUlvIbIwR9R8cd+vkAm7DDhR4Sw2Utmruoyea0TxFSoLHyayfRDB4WEOR6vel0uPJW3fU7h9WSrT+FP6/cqev6dn+0z116uuQIW7modwF1sErGr7uMLt4xUOc00sTag8LA5sIKlOuulsIrDpk8eqQtIRtveT9CPK1R0mLUKHCTwTqwO3pux6sdSgtORoCvNcQbxPTLut8DMlvcb22R3mgIks7GuJ+uD/kHRgyXGPp8/8PxNBV3HR2aTW8qhtK9UdV/3PwMyXtcwAJxOlLH8gFAEuBUiJiFZSi5lFTw5WZwf7EVuSqwMvK6yun06FdIknGgUm1SVVBYVT5APpXB4hsj7nEtvvVXTKxE6BBxXNRb0P6S1oV3v3mKIZ5W1MBPpL1BzfqZZ0CmO+RmQ+NiayjccSNclVIuRvJJqS9iMyoCsSWZlFiqQdCWmwQ4juXBGZ0tMkvZd47xQtOlcujrf9/sLNVSum+Q0RAL+efgmqBTS7RA3jLjZT/CEtVE5Ot99M1CNXonAf2pVwdOsFnyYWsmUs07Hk4dvp5+EdxkDHwDMxTENTp3kk7QJ8gVhMC/iKpI/aPq1mjn2BAyQ9SiQR2mYif512ILYlpPKWInbEBvk08X6eB5zR22lKZQ21+rfAKWmOlRS1r++gvWLDrMb25yVdSLwHzrOfqH1cjPjeyswCcs3qLEQhc7IVobd4bcOxk+qNJF1re7MRn9Mmbi7wLx5/LHAhocu4I5GJXWKUtZRpns2BLxMWezcSDUY7N52rorZ1b0Js/OQU4O9q+9CK4zvVkk5hTE/c+9PArx3Wk5U1ZZIOsX1A030zjcJ84PW27x64/9lEl+4Xi+co6btEPfQ3B47fC9ja9ptr5lrCLfVsNQV3sZlC0ex0FOHmBnA5sa1faQog6Q5gI7e0Wpb0ESIAPpP+euoHKo4fysUrBVqTaLNVP53zSLoR2M72ven2qoRbXqWI/hTObRlCJu9mR/PU6oR02KRFbCoVeMQhWbVBGnc7cIkL+sYV82xH1LiK0Cs+f9S/SyYzXeRgdRagkFH5mO1b0gfZdcQKex2ilqrMUWR9IkA7jP6u6BWAj9p+/ojP8afEyvVUwumptr40fUB/ggn5nXOJpoK/jvK80lxLEvIrItx02srqjB0KM4BzgD2JBcvvibKAKrebssVKrXPNTCDp57Y3qHjsDtvrDdy3GtHJ/Qip7o7IxC5FyPD8b81c2xONbI16tpqCu9g4o3BJ2rkpoCkcvzeR+S42gtkVLnYawsVrWBQ6pF8h/qeXJDKND46yllIDzaGp7vXGqv+zdIwIN761bR8saU1gdduNJQTpfdcrd7rUYRwyeMxnCCmpxYHziZrWi4iM7Lm2S2vRNYQddiYzbuRgdRYg6dZecCnpAGB923ukAvnLXa4G8AZiC/j1RHNQjwVEMFlXTzrseT6dyEjtSgTF/267tBSgayZ2VCg0ave33eSs9FxCHmwD+u0ZSx2KhqglHXbM04HdCY/uS1OWbWvbJw4ctxeRGV4XKHbWLw/Mr8tEzgQpc/W6wd9VIXf1o7L3dHr8FcQiDOBW2z8pO25gzF101LPtko2dadRBDUAT6gbPJBy9LqQ/U1q61S/pPwl/+ntbnlPRxrO1+sgwgaek+USz3alEh/sewHPrdgu6ziPpC4QCSK/UYleiSauygUrS14gSi1fYfp7C3vU821tUjUnj9iUkr3p9BDsQSYivDBx3M7GQWopwL1zDYdzwFMI5rs5utZMddiYzdtjOlzG/EAFN7/qFwG5lj1WMfckiON+/I2rYHq055qfE9tXBwPOn4Rz+H/Bzwqf8eELD8CqiFGCXFuMvI2ombyIycgcCn605/iYiY7dxur4vcHHDHJ3HDIxfhbTgLHlsZcKB5lQiA9+7rDbT74eK83sjoff69vR+2ZDIFt9BZEpHOddPgcU6jtkeuJ6wZHyAWOQ9sKhft3Ru56fXavF0eTtwfsWxb6u71MzxI2DpDud0Xdn1FuPmp/fp9UQAuSdwSNOY9POmwn1XTMM8byIk874E7ND2NQCuL9x3Y4txNwHLFm4vW/zdCvdfX3Y93W76HjiF0M09lnCzOhI4clG/l/MlX9pecoPV7OAeSR8gLBI3JQlgpxV1XdNPb+wP6aDJOAySnkdkH3Yiat3+nej0LsVhedjLxH5DIZVTmYkdgiOIOtgria2znxHBZpNPd4+n2L5Qkhy1gAdKupRqD/eFtp0y2l921JK+rWGO1mNSZuhQIng6mFgMrAIsJmkP232i6I7t6j8DO0vakPD3huiEbZUtm05sny7pv4n3yAeIoP0WYiExaQt0iuwPnJ1KKFrp2TKcu9hM0VoNwBPqBssSLk5/S7fnERm6Kh4Frldo6BZfsyrpqqFdvBzqD/PSuR2n0Cut46FU2nOjQrLpt0SAV8sQ81xO1JK3VQN4LL2uvWbOVWmnvCEKuqzpepkm4aOSlnHoTD/RcyBpxRbzdDVhyWTGihyszg7eSdTQbUs0+fSE6V9M6CvW0VmTcUiOJ5ox3ktsUTfWntr+HXBkqnfdn6gRHFWwiu0L0tXT0pdaF2u9v6Y6tTslvR/4NeEXXsUCSR8nXt+t0pdW00Kiy5ijCHetFQnpsn+wfVWqTT6ZCgcfSe8D3kfUe0J0BR9t+6sN5zbt2L5R0oEOu8jpZBgN3GHcxWaKzmoAxI7MtsTrACF1dB6wZcXxZ6dLKzy83NEwgedbiU7u9xGqDmvQYDfadR4NpwZwJPBD4GkKLeOdCCOGJo4Drk5JBYhdh2NLjtvKqUHOdjE4XYLIlNdxGt0WK5nMWJFrVuc4Zc00GqFrh8Kp6BBCCuWXxAf7GsQH8CdcUfdXkYk9zS1r5Fqc13/R72d9RPG27TMmDeofvwVwG7ASkclckdAAvari+Fa1pMOOKf7NJN1m+3mFxyprBBVd91s6NdYofLGvcE1920wi6RKinvIaQkbpUts3j3iO+bY37zims7vYTKF+NQATeqBNagCT/ue7fg5IepHtq4c87arnfBZhZbokEXiuQGiN3lVy7KCL2dXEAtJEHXplINllnnT8UGoAafHYk1z7ie3b6o4vjNuU2P0Q0dk/0np+SVcB2w58Dpxnu2qxksmMFTmzOgtIxfGVuF6T8PdDZGG68AWiaWdt2wsA0pb+4emyb8W44+mYie3I5Uxkkwdvm/6ms0nYviZd/QtR31ZLyhJ/EUDSKsA9dYHqEGOKmZSHB5+qZhoRW5k9ehJqAffOAAALlklEQVRZY4HtrVLGawtga+AsScvZfuoIpxlGz3aYbOy0kzJiOzb8z5fxoKRNnSw8JW3G5PdRr+t9R2IBca7t2xR2xwcQddCV3fBdKAk8L2Yi8LwSKAsiB13MlqLfxWxSsDrkPBA1zsWF8x8p1z4dZBmiJtY0CPUr9J973J0uTzzmML0YFUu7oARh+y8KRZZMZlaQg9XZwUuIbcmTgavpFmy8g8jCfImJLExj8NWB7YF1i9uljg7V9xINVH3BaiETuw7R9fomYA1JtZnYrth+a/pif6MbvLkHzq/TwqBrLemwY6ivCVx68GBNOEB9G7hKUu812AE4oe53nEkUupF/ny4rEQuYS0c8zfuA/SW11rNlOEeyacf231IA1qWkBWJX4VRJv0m3V6c/8Ovxb8D/JTLdX5N0J7GI+HjDFnhXOgeeDOFiNuQ8AOdIOpd+NYDasgiF9vHOhFWziLrYU2vq8K8lPpN7n+e9z1Cl66XKI0MyuFjZnJLFSiYzruQygFlACrq2I7KiGxGF8ie7Qcu05vn2c4k265DP9Qvb67Z9TNKXiEzsB0sysQ/brsrEDnt+l7qDXauk31OzMPCAiLhCRqdXS/oNBmpJy7bnhxnTFfXrXm5BBIO9LcZragfPIArr2/mETNjZHhMNXEmHEtu4XbKxM0Kqh1yRKJ0pujFdVzNmKSI7vx7xPridyB4+MnDcrYR5wN8UDZx/AJ5j+7cj/h2ucUHSSdJRTq5kkq6y/eKSMXfZfk7F8/2n7XWmOo+iUe1y4AbCua64Nf9DapB0G7BJb5covX7XFUt2FhXpM+B7hKubgWcQ/Q+1pjKZzLiQg9VZRvrSeTOx/X6QB7T4Wj7HL10h7j3Ec50O/GBw+zqVHuxSkom8k4FMbLp/HnC77eeO4rwKz/tJYjt38Iu9yomn08JgmFrSYetPuzCq55luJK1EKFVsRZQCPE64hn1qhHMMo2fb2V1splA0JA5i26+oGVNmDtF4X9kxo2DIwLOzi1nXeSQdTjSdrU9ISl1BBK9XNm3LK4wX3uzUAJve29+xXWtvLGkHYmF0f2Hc1rZPrxvXhhSk3mP7d5KWAPYidrN+Dnx6xKUGmcy0kcsAZgkpSH0tEUQ9m+g8/UHdmLqnG9FpQWyx/kDSO5jY1tqCqNfaoeR4Dwaq6c6/SZqOldNe6WdRRstAabDu6JY9h9gG7C0MLpJUtTAYppZ02PrTLqwqqUpmaCwahQBs36dohluTaMzbkmYVha58jSij2JjYFj6WKI8oteBM57X8iM9hZNh+edtjFU18zyRKRzZh4n9/BaK+cpD1JfUytALWS7d7wfqoAterJb27IvCskon6IHC6pN0pcTEbxTy2P5IeX5IwHNiSKKX6pqT7XOG6lngEuFVSz8Z0W+AySUem5y41YAA+U8zapv+JzzCh4DEVjknnAVFOdgAhFfcCYldnpxHMkclMOzlYnQVIOoEQTf8xoRV6yxSfcmRBoe1fAy/ShLOQgB/bvrBiyM9TXWZZJvb2UZ1X4fzW7Dqm48KgUy3pFMZ0ZR5Rlzc2zVRlKJyS7iD0f78O7DkNpQCdNXCHycZON3WLD6hcgLyKMA1Yg9TMl1hABC6DjKSBqgWdA09Hw9OW6ncxO8v1LmbDBLgQi+0ViHKLFYnt8yaVinMJibDHCa3Usgx4GWWNW6P6bp5XyJ7uSjhjfR/4vqQbRjRHJjPt5DKAWYCkx5nYwi7+weo8zhdQHpSKELxfJAsVSc8kAr+HKcnEpuB31HOuz2Tb1JMqji0uDL43goXBImG6tm9HjaTF3K8ZOR1zXExky/ckyg1+TwSidT7vNxHOYhsRWdhjgTfZrszGTjcp2wZRd7oFE4oWryNqKt9VM3ZHd2s0PMQD9qVl900VDWGfO53zSPpGOm4BUbN+FXCVw2Sj6rmL8n3/QwSfaxINXAe4oWlU0rcIp72jic/DDwAr235729+v5rlvAV5ge6Gk24H32L6k95jtDac6RyYzE+RgNbNIGMjE3lqTiZ3qPJ8EXknUoJ1LZJous/2miuM7LwzGkVlUs9ra534KcwyjgXud7U0VHd6/TtnYsVgASDqPkK/qNSguD5xq+9Ulx77F9nckfZiSxWtVOUhFPeskzea5hqRzCGWOW4h61StpMIdQfdPoQ7ZL3cUK45cFPkVs14swa/ic7QfrxrX8fT4BvIZolFsL2DTtMjwHOMH2S6c6RyYzE+RgNTOnkXQzUZ91ne2NJa0OHDPY+DXX0Oh1GqeFVN93EpG9hHDz+kfbo3ZY6823CvDHuuAjHdc5GztTpAzZxk6d/Kls5Ubb65ccu5ftYwpZ2SK2fdDg8cDewLpEeUaP5YH5ZU1Mcw1JIhbSW6bLhoTM3JW2J72OmuGm0a4opPJWJ0wAHkz3rQss5xoFiUxmnMg1q5m5zsOpeWthykD9jtHqF44lsyFQTbT2ue+KhtOz7bErkY19Z+qkXotQ4BgHvg38TGHPaaKRsSpLfBaA7c8OPiDpdSXHn0LUXf4L8LHC/Qs8Ine5cScFnbdIug+4P122B14IVAX9QzeNpsDxI0R9/BPfya5Rd+iCS1z3bP9iFM+dycwUObOamdNIOgb4Z+AfgX2AB4DbbO+xSE8sA4CkCwg3s6LD2p62t6kc1P65R6Jn2zYbO5Mo7Dl7+sGV9pyS7gBeZfvugfv3BD7pEomowjEbEjqjEDa4Q+k6zyYk7UNkU19KSJZdTpQCXA7cXFZfrY7yfSXjbySaC68lGrMAcNZAzWSeIAermScNqU5rhbz1NT6o3Od+n1F03Ws4DdzKbCzQlI2dMRTOX8+1fZzCt3452/9dctxrgC8Dr7F9Z7rv40TW+B+qaoMlvY+QpevJJ70BONr2V0f/24wPkr5I0lZ1SyOEqTaNSrrW9mZTOvFMZo6Tg9XMnEfSbsA6tj8vaU1gtZy1GF80Ioc19bt4tRK7H1U2djpJ9aebA+vZXlfSM4gGq9JmGUnbEHqbbwTeRQRS2zd0uN8EbOnkJy9pOeAK2xuN9reZOwzbNCrpQOBe4IeEViswq0p5MplpJwermTmNpKMIkfmtbD9P0lOBc12wYMyMFxqRw5rCyvVBklwb8FDvIWBp25PMB4bJxs40SR9zE6JpcJN03011gWTKxJ5OZA13cbIErTn+ZmDzgSau+ePQYDbXkDQpI07Uwc752vpMpi25wSoz19kySRBdD5GtULjTZMaXkRgZ2J43xLCZcBebKo8m+SHDE9JHpWhCb1mEEP42wL2p432SFJukxW0vJMofrpLU02bdAThh9L9Kxvbai/ocMplxp8w5I5OZSzwmaTFSoCHp/9AfkGTGj0UZFG4s6YEU5G2Urvduj0tW8ZTUOLiSpHcDFwDfLDvQ9vK2V0g/l7S9bOF2mWbwz9K4w4D3ENnoh4G9bR8+Pb/OkxNJ+xeu7zzw2CEzf0aZzPiSywAycxpJexBZoc2BbwG7EJa131ukJ/YkR2PqsDZbkLQdYXYhoqzl/IYhbZ93LEodngwMU1OdyTxZyV8ImTmJpLOBf7J9oqRrmXCH2dmz1EJ1LmF7+UV9DrOZFJye35PVGuFTryrpQzXzljpeZYZCFdfLbmcyT2pysJqZqxwPnCfpBOCwJ4NGZGZuM0WTg7bMA5YjB0szgSuul93OZJ7U5DKAzJwlNZ58Gng18cX+RK1qzhBlZhszIauVt59njmHUKjKZJys5s5qZyzxGfBksRXib58aqzGxmcdvnAUg6qGejafv2aO4fCTmjOkMMqVaRyTwpycFqZk4i6dXAF4EzgE1tP9QwJJMZd2ZCVmvKNreZTCYzanIZQGZOIulSQm4n16pm5gR52ziTyTxZycFqJpPJZDKZTGZsyaYAmUwmk8lkMpmxJQermUwmk8lkMpmxJQermUwmk8lkMpmxJQermUwmk8lkMpmxJQermUwmk8lkMpmxJQermUwmk8lkMpmx5f8DjI4hpq13qwAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9beae2780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9bee33da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 观察特征X的两两相关性，画出热点图 \n",
    "corrmat =X.corr()  \n",
    "f, ax = plt.subplots(figsize=(12, 9))  \n",
    "sns.heatmap(corrmat, vmax=0.8, square=True)  \n",
    "plt.show()  \n",
    "  \n",
    "# 取10个变量进行热点分析  \n",
    "k = 10 # number of variables for heatmap  \n",
    "cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index  \n",
    "cm = np.corrcoef(X[cols].values.T)  \n",
    "sns.set(font_scale=1.25)  \n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f',  \n",
    "                 annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图1看出颜色越趋于白色的地方表示相关性越高。可以看到1stFlrSF, GarageArea, TotRmsAbvGrd, GarageYrBlt与一些特征相关性很大，可以考虑删除。\n",
    "从图2小图看出确实上述几个特征相关性程度很高，基本在0.8以上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data=all_data.drop(['1stFlrSF', 'GarageArea', 'TotRmsAbvGrd', 'GarageYrBlt'], axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>...</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>...</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
       "0   1          60       RL         65.0     8450   Pave      Reg         Lvl   \n",
       "1   2          20       RL         80.0     9600   Pave      Reg         Lvl   \n",
       "2   3          60       RL         68.0    11250   Pave      IR1         Lvl   \n",
       "3   4          70       RL         60.0     9550   Pave      IR1         Lvl   \n",
       "4   5          60       RL         84.0    14260   Pave      IR1         Lvl   \n",
       "\n",
       "  Utilities LotConfig      ...       EnclosedPorch 3SsnPorch ScreenPorch  \\\n",
       "0    AllPub    Inside      ...                   0         0           0   \n",
       "1    AllPub       FR2      ...                   0         0           0   \n",
       "2    AllPub    Inside      ...                   0         0           0   \n",
       "3    AllPub    Corner      ...                 272         0           0   \n",
       "4    AllPub       FR2      ...                   0         0           0   \n",
       "\n",
       "  PoolArea Fence MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
       "0        0   NaN       0       2    2008        WD         Normal  \n",
       "1        0   NaN       0       5    2007        WD         Normal  \n",
       "2        0   NaN       0       9    2008        WD         Normal  \n",
       "3        0   NaN       0       2    2006        WD        Abnorml  \n",
       "4        0   NaN       0      12    2008        WD         Normal  \n",
       "\n",
       "[5 rows x 73 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算上之前对丢失值率超过90%的，特征X去除了7个维度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.4对于丢失数据的进行添补\n",
    "  这部分很复杂，由于丢失的数据很多，X特征变量有类别型的，也有数据型的。对于两个变量相关的，如果能找出其中的对应关系，或者对应的函数特征，可以构造相对应的函数，然后通过函数算出结果进行填充。\n",
    " 1、具有相关关系的变量填充；\n",
    " 1）MasVnrType和MasVnrArea；\n",
    " 2）LotArea对LotFrontage\n",
    " 3）含有Bsmt和Garage相关的；\n",
    " \n",
    " 2、其他数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对于MasVnr数据填充，MasVnrType和MasVnrArea两个数据相关，缺少type的填充None，缺少面积的补0。\n",
    "all_data[\"MasVnrType\"] = all_data[\"MasVnrType\"].fillna(\"None\")\n",
    "all_data[\"MasVnrArea\"] = all_data[\"MasVnrArea\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LotArea对LotFrontage进行填充，构造一个多项式，然后通过多项式进行填充数据。\n",
    "x = all_data.loc[np.logical_not(all_data[\"LotFrontage\"].isnull()), \"LotArea\"]\n",
    "y = all_data.loc[np.logical_not(all_data[\"LotFrontage\"].isnull()), \"LotFrontage\"]\n",
    "t = (x <= 25000) & (y <= 150)\n",
    "#拟合多项式以填充缺失值\n",
    "p = np.polyfit(x[t], y[t], 1)\n",
    "#根据多项式求函数值\n",
    "all_data.loc[all_data['LotFrontage'].isnull(), 'LotFrontage'] = np.polyval(p, all_data.loc[all_data['LotFrontage'].isnull(), 'LotArea'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对于BSMT和Garage类，所有类别型填充None，所有数值型填充0\n",
    "alldata_columns = pd.Series(all_data.columns)\n",
    "BsmtList = alldata_columns [alldata_columns .str.contains('Bsmt')].values\n",
    "GarageList = alldata_columns[alldata_columns.str.contains('Garage')].values\n",
    "Bsmt_cat=all_data[BsmtList].select_dtypes(exclude=[np.number]).columns\n",
    "Bsmt_num=all_data[BsmtList].select_dtypes(include=[np.number]).columns\n",
    "Garage_cat=all_data[GarageList].select_dtypes(exclude=[np.number]).columns\n",
    "Garage_num=all_data[GarageList].select_dtypes(include=[np.number]).columns\n",
    "all_data[Bsmt_cat]=all_data[Bsmt_cat].fillna('NoBsmt')\n",
    "all_data[Bsmt_num]=all_data[Bsmt_num].fillna(0)\n",
    "all_data[Garage_cat]=all_data[Garage_cat].fillna('NoGarage')\n",
    "all_data[Garage_num]=all_data[Garage_num].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#针对FireplaceQu，Fence，MSSubClass的填充；\n",
    "all_data = all_data.fillna({\n",
    "    'FireplaceQu': 'NoFireplace',\n",
    "    'Fence' : 'NoFence',\n",
    "    'MSSubClass':'None'\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查询一下还有哪些数据缺失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>4</td>\n",
       "      <td>0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Total   Percent\n",
       "MSZoning          4  0.001370\n",
       "Utilities         2  0.000685\n",
       "Functional        2  0.000685\n",
       "KitchenQual       1  0.000343\n",
       "SaleType          1  0.000343\n",
       "Exterior1st       1  0.000343\n",
       "Electrical        1  0.000343\n",
       "Exterior2nd       1  0.000343\n",
       "MasVnrType        0  0.000000\n",
       "YearRemodAdd      0  0.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total =all_data.isnull().sum().sort_values(ascending=False)\n",
    "percent = (all_data.isnull().sum()/all_data.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现还有MSZoning，Utilities，Functional，KitchenQual，SaleType，Exterior1st，Electrical，Exterior2nd这几个特征有丢失\n",
    "  其中MsZoning可根据MSSubClass填充；\n",
    "  Functional是类别型，常用Typ，使用其填充\n",
    "  其他几个变量可使用众数，也可以使用平均值进行填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MSZoning利用MSSubClass进行分组之后再进行填充\n",
    "all_data[\"MSZoning\"] = all_data.groupby(\"MSSubClass\")[\"MSZoning\"].transform(lambda x: x.fillna(x.mode()[0]))\n",
    "#Functional  填Typ\n",
    "all_data[\"Functional\"] = all_data[\"Functional\"].fillna(\"Typ\")\n",
    "#其他那些数据采用众数填充\n",
    "all_data['Electrical']=all_data['Electrical'].fillna(all_data['Electrical'].mode()[0])\n",
    "all_data['KitchenQual']=all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0])\n",
    "all_data['Exterior1st']=all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode()[0])\n",
    "all_data['Exterior2nd']=all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode()[0])\n",
    "all_data['SaleType']=all_data['SaleType'].fillna(all_data['SaleType'].mode()[0])\n",
    "all_data['Utilities']=all_data['Utilities'].fillna(all_data['Utilities'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SaleCondition</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Total  Percent\n",
       "SaleCondition      0      0.0\n",
       "BsmtFinSF2         0      0.0\n",
       "YearRemodAdd       0      0.0\n",
       "RoofStyle          0      0.0\n",
       "RoofMatl           0      0.0\n",
       "Exterior1st        0      0.0\n",
       "Exterior2nd        0      0.0\n",
       "MasVnrType         0      0.0\n",
       "MasVnrArea         0      0.0\n",
       "ExterQual          0      0.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检查是否还有缺失值\n",
    "total =all_data.isnull().sum().sort_values(ascending=False)\n",
    "percent = (all_data.isnull().sum()/all_data.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3、特征工程 \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看填补数据的维度\n",
    "1，分别列出数字型特征和类别型特征，并打印出数量\n",
    "2，对数字型特征进行log1p 使其更加正态化\n",
    "3，对类别型的特征进行one-hot编码\n",
    "4，连接转换完成的类别型特征和数值型特征，组成新的特征集合\n",
    "5，分为特征X，y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 73)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看all_data的数据维度\n",
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical features : 33\n",
      "Categorical features : 40\n"
     ]
    }
   ],
   "source": [
    "#分别指出数字型特征和类别型的特征\n",
    "categorical_features = all_data.select_dtypes(include = [\"object\"]).columns  \n",
    "numerical_features = all_data.select_dtypes(exclude = [\"object\"]).columns  \n",
    "# numerical_features = numerical_features.drop(\"SalePrice\")  \n",
    "print(\"Numerical features : \" + str(len(numerical_features)))  \n",
    "print(\"Categorical features : \" + str(len(categorical_features)))  \n",
    "all_data_num = all_data[numerical_features]  \n",
    "all_data_cat = all_data[categorical_features]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对目标y，和数字型的特征进行log1p 使其更加正态化\n",
    "from scipy.stats import skew\n",
    "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n",
    "\n",
    "#log transform skewed numeric features:\n",
    "numeric_feats = all_data.dtypes[all_data.dtypes != \"object\"].index\n",
    "\n",
    "skewed_feats = train[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness\n",
    "skewed_feats = skewed_feats[skewed_feats > 0.75]\n",
    "skewed_feats = skewed_feats.index\n",
    "\n",
    "all_data[skewed_feats] = np.log1p(all_data[skewed_feats])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对类别型的数据进行one-hot编码\n",
    "all_data = pd.get_dummies(all_data_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#连接转换完成的类别型编码和原来的数值型编码\n",
    "all_data1=pd.concat([all_data, all_data_num], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 287)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看组合完成后的数据维度\n",
    "all_data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将组合完成的数据分为X_train,X_test,y\n",
    "X_train = all_data1[:train.shape[0]]\n",
    "X_test = all_data1[train.shape[0]:]\n",
    "y = train.SalePrice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4、模型预测和评价\n",
    "\n",
    "   定义评价函数\n",
    "   使用最小二乘，岭回归,lasoo,注意正则参数的调优。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义评价函数RMSE\n",
    "from sklearn.model_selection import cross_val_score\n",
    "def rmse_cv(model):\n",
    "    rmse= np.sqrt(-cross_val_score(model, X_train, y, scoring=\"neg_mean_squared_error\", cv = 5))\n",
    "    return(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -5.36262926e+00,  -5.32523083e+00,  -5.32620560e+00,\n",
       "        -5.32717193e+00,  -5.33102993e+00,   2.84282302e+04,\n",
       "         2.84282374e+04,  -2.43432734e+05,  -2.43432732e+05,\n",
       "        -2.43432732e+05,  -2.43432734e+05,   1.17306580e+05,\n",
       "         1.17306582e+05,   1.17306579e+05,   1.17306582e+05,\n",
       "         6.62323650e+04,   6.62323461e+04,  -6.22397229e+03,\n",
       "        -6.22397048e+03,  -6.22397545e+03,  -6.22397943e+03,\n",
       "        -6.22397349e+03,   1.20205015e+03,   1.20205228e+03,\n",
       "         1.20203292e+03,  -6.14605279e+02,  -6.14608678e+02,\n",
       "        -6.14610561e+02,  -6.14604692e+02,  -6.14603711e+02,\n",
       "        -6.14606507e+02,  -6.14596541e+02,  -6.14611894e+02,\n",
       "        -6.14606886e+02,  -6.14606589e+02,  -6.14617882e+02,\n",
       "        -6.14609324e+02,  -6.14608183e+02,  -6.14605402e+02,\n",
       "        -6.14608614e+02,  -6.14603659e+02,  -6.14599042e+02,\n",
       "        -6.14608492e+02,  -6.14605016e+02,  -6.14607901e+02,\n",
       "        -6.14606024e+02,  -6.14602981e+02,  -6.14595326e+02,\n",
       "        -6.14604888e+02,  -6.14601722e+02,  -1.11025083e+02,\n",
       "        -1.11021619e+02,  -1.11018079e+02,  -1.11022303e+02,\n",
       "        -1.11018123e+02,  -1.11027146e+02,  -1.11021378e+02,\n",
       "        -1.11023607e+02,  -1.11018095e+02,  -1.83685694e+02,\n",
       "        -1.83675006e+02,  -1.83680528e+02,  -1.83663959e+02,\n",
       "        -1.83744416e+02,  -1.83724383e+02,  -1.83688288e+02,\n",
       "        -1.83681116e+02,  -1.12973643e+02,  -1.12969302e+02,\n",
       "        -1.12973345e+02,  -1.12977417e+02,  -1.12974503e+02,\n",
       "        -3.25024040e+02,  -3.25024229e+02,  -3.25026618e+02,\n",
       "        -3.25030964e+02,  -3.25020843e+02,  -3.25025589e+02,\n",
       "        -3.25024732e+02,  -3.25023672e+02,  -6.68855857e+02,\n",
       "        -6.68857054e+02,  -6.68856262e+02,  -6.68856839e+02,\n",
       "        -6.68852338e+02,  -6.68820391e+02,  -1.35590932e+02,\n",
       "        -1.35398040e+02,  -1.35366299e+02,  -1.35376505e+02,\n",
       "        -1.35395792e+02,  -1.35396170e+02,  -1.35403057e+02,\n",
       "        -1.35394283e+02,  -5.46549514e+02,  -5.46550268e+02,\n",
       "        -5.46568708e+02,  -5.46543482e+02,  -4.87893603e+02,\n",
       "        -5.46557204e+02,  -5.46551399e+02,  -5.46548463e+02,\n",
       "        -5.46546712e+02,  -5.46550907e+02,  -5.46548541e+02,\n",
       "        -5.46548628e+02,  -5.46551224e+02,  -5.46553817e+02,\n",
       "        -5.46550634e+02,   8.14618815e+01,   8.14669416e+01,\n",
       "         8.14665605e+01,   8.14605495e+01,   2.28016166e+01,\n",
       "         8.14738061e+01,   8.14653375e+01,   8.14657842e+01,\n",
       "         8.14629160e+01,   8.14577640e+01,   8.14652185e+01,\n",
       "         8.14592555e+01,   8.14646804e+01,   8.14670522e+01,\n",
       "         8.14680898e+01,   8.14649650e+01,  -2.49912090e+02,\n",
       "        -2.49908613e+02,  -2.49909183e+02,  -2.49907929e+02,\n",
       "         5.67395753e+02,   5.67397517e+02,   5.67396230e+02,\n",
       "         5.67396945e+02,   1.06235930e+02,   1.06227976e+02,\n",
       "         1.06230506e+02,   1.06231544e+02,   1.06232199e+02,\n",
       "         5.14335445e+02,   5.14336820e+02,   5.14338224e+02,\n",
       "         5.14332551e+02,   5.14343987e+02,   5.14326957e+02,\n",
       "         3.29454171e+02,   3.29452656e+02,   3.29452281e+02,\n",
       "         6.31706579e+02,   3.29452342e+02,   3.61154521e+02,\n",
       "         3.61156540e+02,   3.12630731e+02,   3.61178810e+02,\n",
       "         3.61156434e+02,  -2.67662360e+02,  -2.67660295e+02,\n",
       "        -2.67662595e+02,  -2.67663119e+02,  -2.67667214e+02,\n",
       "         5.18212939e+02,   5.18212828e+02,   5.18213920e+02,\n",
       "         5.18210896e+02,   2.64497059e+02,   5.18212376e+02,\n",
       "         5.18212156e+02,  -1.46324174e+02,  -1.46329753e+02,\n",
       "        -1.46323714e+02,  -1.46327423e+02,  -1.46334475e+02,\n",
       "        -1.46326965e+02,  -1.46325806e+02,   9.00317118e+01,\n",
       "         9.00431891e+01,   9.00496410e+01,   9.00284268e+01,\n",
       "         9.00465350e+01,   9.00496431e+01,   2.88101364e+02,\n",
       "         2.88099692e+02,   2.88099702e+02,   2.88091201e+02,\n",
       "         2.88098906e+02,  -1.42292885e+03,  -1.42292352e+03,\n",
       "         4.38729260e+02,   4.38728915e+02,   4.38722711e+02,\n",
       "         4.38708087e+02,   4.38728079e+02,  -2.74861007e+01,\n",
       "        -2.74905852e+01,  -2.74911248e+01,  -2.74911428e+01,\n",
       "         2.21038270e+01,   2.20822071e+01,   2.21067519e+01,\n",
       "         2.21058148e+01,   2.20986584e+01,   2.20797299e+01,\n",
       "         2.21082768e+01,  -1.08956661e+03,  -1.08956690e+03,\n",
       "        -1.08956496e+03,  -1.08956700e+03,  -1.08956340e+03,\n",
       "        -1.08956495e+03,   2.55240872e+02,   2.55247501e+02,\n",
       "         2.55247323e+02,   2.55246018e+02,   2.55249588e+02,\n",
       "         2.55247386e+02,  -2.52335108e+02,  -1.37367984e+03,\n",
       "        -2.81239383e+02,  -1.37367947e+03,  -1.37368053e+03,\n",
       "         2.02486937e+02,   2.02458432e+02,   2.02464220e+02,\n",
       "        -2.76153922e+02,   2.02457979e+02,   2.02462636e+02,\n",
       "         1.85030240e+01,   1.85202157e+01,   1.85245199e+01,\n",
       "        -8.77238017e+01,   1.85336921e+01,   1.85223310e+01,\n",
       "        -1.13480438e+03,  -1.13480491e+03,  -1.13480319e+03,\n",
       "         1.73765427e+02,   1.73762812e+02,   1.73765133e+02,\n",
       "         1.73764138e+02,   1.73766304e+02,   3.52199749e+02,\n",
       "         3.52203612e+02,   3.52206581e+02,   3.52210310e+02,\n",
       "         3.52196078e+02,   3.52200288e+02,   3.52204686e+02,\n",
       "         3.52204660e+02,   3.52197881e+02,  -7.80565771e+02,\n",
       "        -7.80557395e+02,  -7.80561335e+02,  -7.80564146e+02,\n",
       "        -7.80560712e+02,  -7.80564041e+02,  -4.46807462e-07,\n",
       "        -3.27810519e-05,   1.39378299e-05,   2.00693876e-07,\n",
       "         3.18363718e-03,   2.87854296e-03,   1.29646287e-04,\n",
       "         6.35448232e-05,   7.10278982e-07,   4.84168337e+05,\n",
       "         4.84168337e+05,   4.84168337e+05,  -4.84168337e+05,\n",
       "        -1.66126119e-06,  -3.33703792e-06,   1.94249005e-05,\n",
       "         2.05468636e-03,   3.59951523e-04,   1.18339165e-03,\n",
       "         1.59357756e-03,   7.48863642e-04,  -3.07446627e-03,\n",
       "         3.93913051e-04,   3.28407712e-03,   7.56199006e-06,\n",
       "         3.60961712e-06,   1.02013746e-05,   1.11215395e-05,\n",
       "         2.01118837e-05,   1.07963042e-05,  -6.42030500e-08,\n",
       "        -5.03854490e-05,  -1.68619370e-04])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y)\n",
    "\n",
    "#显示特征的回归系数\n",
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1120.1454106843707"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#评价函数\n",
    "rmse_cv(lr).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.010767013103751912"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9c127fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用L2正则，岭回归进行预测\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "reg = RidgeCV(alphas=alphas, store_cv_values=True)   \n",
    "reg.fit(X_train, y)\n",
    "alphas = [0.05, 0.1, 0.3, 1, 3, 5, 10, 15, 30, 50, 75]\n",
    "cv_ridge.plot(title = \"Ridge_Re\")\n",
    "plt.xlabel(\"alpha\")\n",
    "plt.ylabel(\"rmse\")\n",
    "#评价函数\n",
    "rmse_cv(reg).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9c12bc5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import  LassoCV\n",
    "\n",
    "mses = np.mean(model_lasso.mse_path_, axis = 1)\n",
    "model_lasso= LassoCV(alphas = [0.05, 0.1, 0.3, 1, 3, 5, 10, 15, 30, 50, 75]).fit(X_train, y)\n",
    "plt.plot(np.log10(model_lasso.alphas_), mses) \n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.015264310236768627"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(model_lasso).mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
